Next Article in Journal
Energy Savings in Buildings Based on Image Depth Sensors for Human Activity Recognition
Previous Article in Journal
Economic Growth and Pollution Nexus in Mexico, Colombia, and Venezuela (G-3 Countries): The Role of Renewable Energy in Carbon Dioxide Emissions
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector

Faculty of Engineering Rijeka, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Energy Platform Living Lab, Unska 3, 10000 Zagreb, Croatia
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Ul. Ivana Lučića 5, 10000 Zagreb, Croatia
Author to whom correspondence should be addressed.
Energies 2023, 16(3), 1077;
Received: 30 November 2022 / Revised: 6 January 2023 / Accepted: 12 January 2023 / Published: 18 January 2023


There is an ongoing, revolutionary transformation occurring across the globe. This transformation is altering established processes, disrupting traditional business models and changing how people live their lives. The power sector is no exception and is going through a radical transformation of its own. Renewable energy, distributed energy sources, electric vehicles, advanced metering and communication infrastructure, management algorithms, energy efficiency programs and new digital solutions drive change in the power sector. These changes are fundamentally altering energy supply chains, shifting geopolitical powers and revising energy landscapes. Underlying infrastructural components are expected to generate enormous amounts of data to support these applications. Facilitating a flow of information coming from the system′s components is a prerequisite for applying Artificial Intelligence (AI) solutions in the power sector. New components, data flows and AI techniques will play a key role in demand forecasting, system optimisation, fault detection, predictive maintenance and a whole string of other areas. In this context, digitalisation is becoming one of the most important factors in the power sector′s transformation process. Digital solutions possess significant potential in resolving multiple issues across the power supply chain. Considering the growing importance of AI, this paper explores the current status of the technology’s adoption rate in the power sector. The review is conducted by analysing academic literature but also by analysing several hundred companies around the world that are developing and implementing AI solutions on the grid’s edge.

1. Introduction

Digitalisation is fundamentally transforming the world we live in. It has a growing impact on society, affecting people both at national and international levels [1]. Nationally, digitalisation and the accompanying process of automation are likely to cause a shift from low-skilled to high-skilled occupations [2]. Internationally, similar to the shift towards renewable energy, it may alter the delicate power balance among nations. Digitalisation has been affecting both our personal and professional lives for years and will continue to do so with even stronger implications in the future [3]. Similarly, the energy sector has been going through a transformation of its own [4]. After the dissolution of vertically integrated utility business models [5], the energy sector has been presented with a new set of challenges regarding environmental issues and the surge of renewable energy sources [6]. The phase of reaching a zero-carbon power system is still far from completion, yet we are now witnessing a dawn of a new era [7]. It is hard to define how this new period might be labelled, but what’s certain is that it will be shaped by the progress of digitally-enabled solutions [8]. During the following years, digitalisation will be a key facilitator of the new energy transformation [9]. The energy system is a complex behemoth consisting of countless elements, all working together to provide a reliable supply of energy in real-time. In the future, this system will become even more complex as the traditional centralised flow of energy is disrupted by the emergence of new distributed sources [10]. Electric vehicles (EVs), battery systems, smart appliances and the electrification of buildings will put additional strain on the distribution part of the power supply chain by adding new components, introducing new market players and facilitating bi-directional flows of energy [11].
The intricacies that arise when trying to maintain the delicate supply-demand balance will require the utilisation of complex, automated algorithms that are able to optimise the system in real-time [12]. In order to make informed decisions, these algorithms will need to process vast amounts of data. In this sense, the relatively recent rise of supercomputing and Big Data has empowered the development of various solutions based on Artificial Intelligence (AI) [13]. As the power supply chain gradually becomes more complex, the application of automated, advanced techniques in system management will become a necessity [14]. It is important to note that this will not be an unimaginable leap as techniques such as artificial neural networks (ANNs), reinforcement learning (RL), genetic algorithms (GAs) and multi-agent systems are all commonly explored as tools in forecasting, optimisation and control of the power system [15]. Currently, due to the lack of advanced automation infrastructure, a number of system operations are still performed at basic levels of automation [16]. According to a growing number of research papers, technical reports and case studies, AI will play a crucial role in the power system of the future, limiting manual interventions and introducing advanced techniques of system optimisation [17]. At present, most papers study the application of AI in the process of power flow optimisation. In this sense, AI will essentially be used to convert data gathered from the power system into useful information, which will then be used to formulate concrete steps in optimising system operation. However, as evidenced by various research papers and numerous start-ups, AI’s role in the power system will not be limited to power flow optimisation. Areas such as fault detection, predictive maintenance, demand response, forecasting and customer relations all have significant potential for AI implementation.

Motivation, Related Works and Scope

AI-based solutions are increasingly utilised across various segments of the power sector. The expanding interest in AI can also be evidenced by the surge of research interest in the field. The aforementioned trend necessitates the need to create a systematic overview of the application fields of AI in the power sector. In this context, there are a number of review papers that explore AI approaches to solving issues related to the power system. Ahmad et al. provide a comprehensive review of AI in energetics systems identifying seven key areas of its application [18]. In another paper, Ahmad et al. explore recent advances in AI technology and focus on a narrower application of AI in balancing supply and demand and generating power from solar and hydrogen [19]. Considering papers with slightly higher impact factors, we identified another two comprehensive reviews that look into the entire power sector. Ali et al. analysed AI techniques supporting distributed smart grids [16], while Kumbhar et al. focused on machine learning (ML) applications in the smart grid and the renewable energy sector [20]. Given the large number of areas in which AI can aid the power sector, the majority of reviews focus on a certain aspect of potential AI application rather than taking a holistic approach to the matter. Antonopoulos et al. focus on ML techniques used in demand response [21], Zhang et al. provide a review on the deep learning (DL) approach in frequency analysis and system operation management [22], while Hou et al. [23] and Yazici et al. [24] explore demand forecasting techniques. Other notable reviews look into AI applications regarding the performance of clean energy communities [25], integration of renewable energy sources (RES) in the existing power system [26,27,28], supply chain automation [29], smart buildings [30], implementation of smart meter infrastructure [31], regulatory issues that AI technologies face [32] and the data AI uses [33]. Security issues of AI in the power system are a standalone field that requires a focus of its own [34,35]. Figure 1 shows the surge of research papers focused on the topic of artificial intelligence. The figure has two parts (left and right). The left part is obtained by listing all Scopus-indexed research papers containing the keyword “artificial intelligence”. The right part is obtained by listing all research papers that contain the keywords “artificial intelligence” and “power system”.
Observing previously published research in the field, a significant number of papers offering reviews on AI-based techniques in the power sector can be found. Given a large number of possible areas of application, the majority of papers focus on a particular segment of AI implementation, such as predictive maintenance, demand response, forecasting energy demand, etc. Despite numerous reviews already published, there is a surprising lack of papers that aim to construct a systematic, high-level analysis of all the main fields of AI applications in the power sector. The main aim of the research presented is to establish what are the key areas of research focus regarding the implementation of AI-based solutions in the power sector. Taking it a step further, the paper aims to integrate the analysis of existing academic knowledge with real-life business applications. Naturally, there is a significant number of large, energy-based companies, such as Enel, Engie, ABB, Schneider, General Electric, Honeywell, etc., that have, in recent years, developed strong AI competencies. However, the research presented in the paper focuses only on companies whose core business is artificial intelligence. Within this framework, over 450 companies across the world were observed during the market analysis part of the research. From these companies, a list of promising start-ups and well-established companies is compiled, giving the answer to which fields of application are getting the most traction. To sum up, and against the aforementioned background, the major contribution of the paper is a systematic review of the main AI-based solutions in the power sector that integrates academic knowledge with practical applications. To the best of our knowledge, this is the most comprehensive review of AI-based companies in the power sector to this date (as far as academic papers are concerned). The following paragraphs have a two-fold goal:
Provide a comprehensive, systematic literature review of the current research in the field of AI solutions in the power sector and identify key areas of the potential application of AI technology in the field.
Taking the scientific literature review further, the paper provides a structured insight into the adoption rate of AI technologies in the power sector by observing the current efforts of established companies and start-ups.
With regards to the power sector, in this paper, we provide (1) a systematic review of the main fields of research regarding AI-based solutions and (2) a comprehensive analysis of current AI applications in real life. The introduction segment opens the topic of AI in the power sector and reveals key aspects of the paper. Paragraph 2 reveals the method used during the analysis. Paragraph 3 offers a literature review and a systematic analysis of the main field of research regarding AI solutions in the power sector. Paragraph 4 analyses the current AI adoption rate in the power sector by observing AI companies. Finally, Paragraph 5 gives a final conclusion to the topic.

2. Method

The first part of the paper presents an academic literature review. Its aim is to outline the most researched topics regarding the application of AI-based solutions in the power sector. The process of identifying relevant literature follows a four-stage progression:
Identification by keywords and titles. The key tool used for identifying relevant literature has been the Scopus database. There were three main criteria during the paper selection process of stage one: journal impact factor, citations and year of publication.
Inclusion of related work. Additional documents were included based on key references in papers from stage one.
Selection by abstract. The abstracts of each paper selected during phase one were examined.
Selection by full text. Each selected paper has been examined. Key methods and focus areas were identified.
During the process, a number of queries were utilised. Search queries were formed by combining several of the key search terms from Figure 2. For example: “Artificial Intelligence” (or “Machine Learning” or “Deep Learning”) AND “Forecasting” AND “Demand”. The results obtained have been carefully reviewed and filtered. Our research confirmed the existence of numerous research papers analysing potential AI solutions in the power sector. As the paper focuses on the entire power sector, only a portion of them are referenced. However, the results obtained closely relate to actual research efforts and researchers’ focus areas.
The second part of the paper integrates academic literature findings with a market analysis of real-life applications by firms operating in the sector. The list of companies developing AI solutions was obtained via multiple sources. Some companies are listed in other research papers, such as [21] and [36]. Some companies are listed in various databases, such as ai-startups [37], tracxn [38], Omdena [39], startus-insights [40], and crunchbase [41]. A number of companies were identified via news segments and web pages. Overall, around 500 start-ups and companies were examined during the market analysis. After disregarding inactive companies and identifying the ones whose core business is AI, a list of 220 companies was formed. To this date, and to the best of our knowledge, the list presented in Appendix A is the most extensively published in any research paper.

3. Research on AI Approaches in the Power Sector

As said by one of the bibles of AI [42], “we call ourselves Homo sapiens—man the wise—because our intelligence is so important to us”. The study of AI does not simply aim to develop an understanding of how our cognitive reasoning works but to replicate its processes in order to automate solving complex issues we face on a daily basis. Despite being considered a relatively new field in science, AI has been present in our minds for quite some time before it was established as an academic discipline in the 1950s [43] when Alan Turing published a ground-breaking article in which he described an intelligent machine and proposed a method on how to test its intelligence [44]. However, it remained a somewhat obscure area with limited practical interest up until recent times when advances in the processing power of our computers and the rise of data collection started bringing AI closer to our lives. This is an expansive, ongoing process involving a multidisciplinary approach. The fundamental ideas of AI lie in philosophy and the study of how we perceive our environment, process information and make the “right decisions”, but in order to make a step forward towards standardisation of these concepts, the introduction of logic, probability and computation is needed.
AI is a multidisciplinary domain that utilises various knowledge bases, which range from information technology and engineering to neuroscience or finance. During decades of research, multiple approaches have been used in the AI process. These approaches include but are not limited to, symbolic reasoning [45,46], statistical learning [47,48], Bayesian optimisation [49,50,51], soft computing [52,53,54], logic-based [55,56] and knowledge-based systems [57,58]. Most common, modern AI approaches in tackling power system issues can be divided into four categories [19]: machine learning (ML) [20], multi-agent systems [59,60], nature-inspired intelligence [61] and artificial neural networks (ANN) [62,63,64]. The term artificial intelligence is often interchanged with two other popular concepts: machine learning and deep learning (DL). Although closely related, these techniques are not indistinguishable [65]. Machine learning is a subset of AI and is characterised by a learning process without explicit programming. In other words, ML describes AI methods that can automatically identify data patterns and then use these patterns to forecast future data in an uncertain environment [66,67]. ML can be classified into four categories: supervised, unsupervised, reinforced and evolved learning [68]. Deep learning, another familiar concept in the AI family, is a branch of machine learning methods [69]. DL typically involves the implementation of large neural networks (although this is not a prerequisite) [70]. DL is used to learn multiple levels of representation and abstraction and is developed to have the ability to process raw data [71]. DL gained recognition during the 2012 ImageNet Challenge, after which DNN/CNN/RNN algorithms started to get increasingly used by developers [72]. Figure 3 reveals which of the four main AI techniques are utilised in the papers reviewed in this paper.
As briefly mentioned in the introduction segment, the power sector is currently going through a radical transformation. This transformation is mainly driven by “3Ds”: decarbonisation, decentralisation and digitalisation. In essence, all three processes are heavily linked and interdependent. The power system of the future will be based on renewables and battery storage [73]. Renewable energy relies on distributed generation. In a power system composed of a large number of distributed sources, intelligent (smart) infrastructure plays a key role in optimising resource management. Digitally-enabled solutions are, thus, critical in sustaining our decarbonisation efforts. AI is seen as an emerging technological field, an innovation front and an enabling technology [74]. Advances in computational power and AItechniques enabled AI to play a notable role in global business arenas [75]. AI technology rapidly progresses and is accompanied by a growing research presence—the power sector is not an exception. In this context, AI-aided solutions can find their place in all four segments of the energy supply chain: generation, transmission, distribution and supply [76].

3.1. Forecasting

Reliability is one of the key aspects of a successful energy supply chain. To ensure grid stability, power consumption and generation need to be in constant equilibrium. Balancing countless components with time-dependent outputs and/or demands is an extremely difficult task to manage. This is particularly emphasised when techno-economic constraints of the generation portfolio are taken into consideration. What further complicates things is the surge of variable RES. These generation capacities have intermittent outputs that are oftentimes difficult to predict and challenging to incorporate in hourly dispatch curves. In addition, the traditional energy system is based on a centralised flow of energy from large producers (typically thermal power plants) to consumers. However, the emergence of distributed energy sources and prosumers is disrupting this paradigm and introducing bidirectional power flows. In this context, the transition from a traditional power system to a decentralised, smart system of the future will necessarily entail the implementation of advanced management software, as shown in Figure 4.
Uncertainty regarding the variability of both demand and generation significantly increases risks and costs associated with the reliable provision of energy. In the past, the main solution to the issue of variability was to install backup generation capacities. However, the rise of digitally-enabled solutions introduces algorithms that aim to forecast the potential supply-demand curve and optimise assets to achieve optimal dispatch. In this context, forecasting has been one of the first areas of AI application in the power sector. Short-term forecasting of electricity demand is already a well-established topic and has been researched for over two decades [77,78]. Studies in short-term demand forecasting have now started to shift towards exploring smart grids and smart buildings [79]. AI techniques are also being applied in forecasting medium and long-term demand levels using different approaches such as fuzzy logic [80], multivariate adaptive regression splines (MARS), artificial neural network (ANN) and linear regression (LR) methods [81].
In addition, the process of predicting generation outputs from variable renewables is receiving an increasing amount of attention. Successful prediction of wind or solar power plant output requires the installation of smart sensors and the integration of this data with GPS-based weather forecasting platforms. AI methods have been researched regarding solar units using the adaptive network-based fuzzy inference system (ANFIS) [82] and utilising ML [83] and ANN approaches [84]. ML [85] and ANN [86] approaches are also used in forecasting wind power production. Long-term forecasts on wind power are also a topic of strong researchers’ interest [87]. There are several studies that confirm that the application of AI-based algorithms is able to notably improve the accuracy of predicting wind [88,89] and solar generation [90,91]. In addition to forecasting demand or production outputs, AI techniques are also commonly explored in order to predict the prices of energy commodities [92] and electricity prices [93,94,95].

3.2. Optimisation

As evidenced by our research, optimisation applications in the power sector can be divided into asset optimisation and system-level optimisation. In this context, asset optimisation refers to the process of operational optimisation of a single unit, such as a power plant, a grid component, a storage system, a home energy management system (HEMS), and/or a building heating, ventilation and air conditioning (HVAC) system. There are numerous papers analysing how to augment the efficiency of variable renewables such as solar [96,97] and wind power plants [98,99]. Other papers focus on traditional power plants [100], grid assets [101] and energy storage systems [102]. Looking at the consumer side of the equation, papers focus on HVAC systems [103], energy efficiency in buildings [104] and HEMSs [105]. In addition to operational optimisation, AI-aided solutions also focus on predictive maintenance and fault detection.
Another large AI research area refers to energy resource management. System-level optimisation focuses on a broader area optimising power flows and dispatch schedules. In this context, there is a considerable amount of papers that focus on demand response (DR) [19]. Some papers focus on the demand response of households [106], some explore buildings [107], while others analyse industrial facilities [108]. In addition, there are three other, relatively novel areas where AI optimisation is researched: virtual power plants (VPPs) [109], battery systems [110] and e-mobility [111]. With the emergence of prosumers and distributed energy sources (DERs), the virtual power plant concept is quickly gaining momentum [112]. AI regarding VPPs is explored in the context of schedule optimising [113] and forecasting flexibility [114]. Considering the surge of distributed sources and variable renewables, AI is increasingly becoming an important segment of system operation and control. Figure 5 reveals a typical fault occurrence in a power grid and distinguishes how primary, secondary and tertiary control help in bringing the system back in balance. It also outlines the main field of operation for an AI-aided system that can automate and optimise the recovery process. AI approaches show a distinct advantage in solving such complex problems in real-time [115].
Regarding battery storage, AI is used to explore digital twins in management systems [116], predict novel materials with designed properties [117] and facilitate the process of searching for novel electrochemical energy storage materials [118]. E-mobility is an important part of almost any sustainable development scenario. However, at present, it seems that the topic of e-mobility is not at the focus of the AI-based research community. In other words, there are not many papers that analyse how AI solutions can be applied to e-mobility. Research in this particular area is mostly focused on analysing how AI can help with electric vehicle (EV) mass adoption [119], how it can help with smart charging [120] and EV energy management and charging scheduling systems [121]. Taking it a step further from Figure 5 and Figure 6 reveals the key building blocks of an AI-aided energy management system that would help optimise the operation of the energy supply chain, taking into consideration the techno-economic aspects of power generation capacities, grid constraints and forecasts.
AI is also heavily researched regarding the topic of predictive maintenance in the power sector [122]. Predictive maintenance of smart grids [123], renewable energy systems [124], photovoltaic systems [125], wind farms [126,127] and distribution networks [128] is explored. In addition to the above-mentioned fields of research, there are numerous other AI-related topics regarding the power sector. AI-based solutions are explored regarding RES sizing options [129], fault detection [130,131], integration of RES [132] and energy efficiency [133].

3.3. Services

Observing literature regarding AI-aided services to consumers in the power sector, an evident gap can be noticed. Other sectors, particularly tech, media and telecom (TMT) and software have done two major things: (1) shifted their focus to being customer-centric companies that aim to provide a premium service [134] and (2) adopted an open innovation business model in which they create to facilitate the co-creation of value for their consumers [135]. Customer engagement via interactive AI platforms is a fast-growing field [136]. A recent study predicts that by 2025, 95% of consumer interactions might be powered by AI [137]. Although this could be hard to achieve, it is certainly an indication of where AI-aided customer engagement is heading. Businesses will have to adapt to the new, service-orientated reality or will risk becoming uncompetitive in the market. Incorporating AI-based customer support platforms is the first step towards creating an open ecosystem able to foster value co-creation. The key will be to engage customers by actively managing their expectations.
Electric utilities are having a difficult time switching their business models to being customer-centric [138] and are finding it even more difficult to create successful ecosystems. In other words, they keep offering a commodity instead of a service. Following suit, research on AI-aided services in the power sector is limited compared to other sectors [139]. Current research on the topic focuses on analysing active consumer participation in smart energy systems [140], assessing service quality and customer satisfaction [141] and analysing customer experience with chat boxes [142]. AI-aided customer relationship management (CRM) will help improve the ability to predict customer value and will improve the treatment of customers.

4. AI Applications in the Power Sector

Everything in AI is about prediction. In one way or another, AI is used to predict the future. It does so by comparing a string of predetermined conditions with two sets of data: past state and current conditions. The changes in data that have occurred during the time interval observed are used to determine the future state of the system. Given a specified set of parameters, AI generates actions to be taken to ensure optimal operation during this future interval. In the power sector, these actions are applied for various purposes, for power flow optimisation, predictive maintenance, security enhancement, customer support etc.
Applying AI-based solutions requires the use of a considerable amount of data. The term big data simply refers to extremely large datasets. Quality data has always been difficult to attain, but with the digitalisation of the energy system, an increasing amount of data is now being generated. According to a report by Cisco [143], in 2018, an average of 5 exabytes (1018 bytes) of data was generated each day. The power and scale of digitalisation are bringing our world closer together, and with increased interconnections and an exponentially larger number of smart devices, the generation of data will only become more significant. Some estimates predict that by 2025 over 460 exabytes of data will be generated each day [144]. When this abundance of information is paired with the increased processing power of today′s computers, an ideal foundation for the application of AI is created. As far as the energy sector goes, there will be several major new sources of data: smart meters installed at consumer, storage and producer locations, electric vehicles and numerous smart devices. The Internet of things (IoT) revolution is knocking on the doors of the energy industry, with estimates predicting growth from 25 billion IoT devices that exist today to 75 billion in just a five-year time frame [145].
Figure 7 presents a basic principle of the transactive energy management model where each physical asset has a corresponding virtual counterpart [135]. Digital twins such as the one presented transact in digital space while recording power flows that occur in the system. The key enabler of such a system is the existence of a digital platform. As shown in Figure 7, this platform would consist of three main layers: physical, infrastructure and business. These layers are further divided into six additional interoperability layers: business, functional, information, communication, integration and asset. The aforementioned layers are built upon the physical infrastructure of the power grid and enable a fully-digital peer-to-peer transaction network.

Power Sector’s AI Adoption Rate

As can be seen from the examples of early adopters, AI is destined to be one of the key enablers of the next wave of digital disruption. Facilitated by the rise of digital platforms and by tech giants such as Google, Facebook and Baidu, investments in AI are growing at a rapid pace. According to a recent report by McKinsey, in 2016, companies around the globe invested between USD 26 and 39 billion in AI [146]. The majority of the funding came from tech giants who invested between USD 20 and 30 billion in research, development and deployment of AI systems. Outside the tech world, the adoption of AI is still in its early stages. Energy companies basically shun away from it despite the myriad of advantages AI might bring to the table. The lethargic approach to developing and adopting new solutions is one of the reasons why most utilities have been underperforming in financial markets [147]. Further neglecting the digital reality will only increase the gap energy utilities face when compared to other businesses, such as software or the tech, media and telecom (TMT) industry [135]. However, despite certain headwinds and the slower rate of adoption, AI-aided solutions in the power sector are a rapidly growing field with different interest areas. Figure 8 reveal major areas of AI application in the power sector.
Currently, the biggest focus is directed towards four major fields of interest:
Optimisation of assets. According to our research, the majority of AI-based companies in the power sector focus on the application of various optimisation techniques. This field refers to the operational optimisation of particular assets within the system, such as renewable power plants, battery systems, buildings′ energy systems and/or home energy management systems. Companies developing applications in this area rely on business models that offer value propositions to residential and commercial users and power plant operators. It should be noted that this field of application does not only deal with power assets. A number of companies analysed use their solutions to integrate home or to build HVAC systems into their optimisation processes. In such a way, they offer an integrated solution to homeowners and companies.
Optimisation at the system level. This field refers to the management of power flows of the grid and tuning the supply-demand balance by adjusting the production of the generation portfolio, optimally utilising energy storage capacities and applying available demand response schemes. Companies that focus on system-level optimisation generally aim at offering their services to grid operators, utilities and power producers. Grid operators use AI-aided solutions to forecast renewable generation and energy demand and then use this data to optimise their dispatching schedules, and grid power flows. Power generation companies form virtual power plants through which they optimise their production schedules and minimise their exposure to market risk.
Data analytics. Data analysis and forecasts present the third largest field of AI application in the power sector. Naturally, all AI-based solutions require the use of some form of data analysis. However, companies listed as “optimisers” in the above two fields analyse data to form actions that automatically influence power assets. Companies in the segment of "Data analytics" form insights that are then used for further analysis and form a base for the decision-making process but do not autonomously optimise infrastructural assets. Companies focused on data analytics generally offer their services to utilities and the commercial sector via various monitoring platforms and to power generation companies and system operators that use data analytics in forecasting renewable generation and energy demand.
Operation and maintenance. O&M is a very common form of AI application in the power sector. Companies that deal with predictive maintenance generally offer their services to power generation companies and the industrial sector. The majority of use cases regarding this field refer to AI-aided analysis of drone or satellite imagery. This field of application also refers to the robotisation of the maintenance process, where (for instance) robots are used to clean PV panels or inspect wind turbines.
Apart from the applications listed in the four fields above, there are several other that have growth potential. The most prolific of these applications are the ones focused on the field of electric mobility. There are numerous companies that use AI-aided solutions regarding electric vehicles. However, in this research, we have considered the ones that focus on applications with a direct influence on the power grid. These generally include companies that develop platforms for the optimisation and/or integration of charging infrastructure and that analyse how to use renewable energy for the charging process. Companies that focus on autonomous driving, upgrading EV efficiency and/or improving battery life have been disregarded. Despite not making it into the top four applications, E-mobility is already on its way to widespread adoption. Looking at AI companies tied to the power sector, we believe that the future of the automotive industry is electric. However, this is a topic for another paper. Sixth on the list seems to be the application of customer service platforms. These help energy providers to understand and serve their customers, offering them a premium customer journey. In this context, the majority of companies use AI to unlock meter-level data, provide consumer insights and make business processes automated and smarter for energy retailers, utilities and grid operators.
Apart from the aforementioned fields, and as witnessed by our research, other popular areas of AI application in the power sector are cybersecurity and financial services. Cybersecurity is a key prerequisite for the enablement of AI. The increased data flows will inevitably raise associated security risks. As the future smart grid will become increasingly distributed, and as it will consist of countless IOT components and smart appliances, so will the entry points for malicious attacks increase. Applying AI-based architecture such as artificial neural networks (ANNs) is proving to mitigate the cybersecurity susceptibilities of the power system. Finally, the financial service segment of AI-aided applications in the power sector will ensure that distributed sources are more easily financeable. At present, around 30–40% of rooftop solar costs refer to soft costs. Aiding the design process and offering a financing solution can prove crucial in the more widespread adoption of solar rooftop solar panels, whether regarding households or commercial users. Figure 9 shows the spatial distribution of analysed companies.

5. Conclusions

The surge of distributed energy sources is causing fundamental changes to the traditional, centralised flow of energy in the power system. Variable renewable energy sources such as wind and solar are disrupting both the energy supply chain and the business models of the companies in the power sector. The increasing amount of energy being produced by these variable sources, paired with the gradual inclusion of electric vehicle charging stations and battery systems, significantly complicates the delicate supply-demand balance of power supply. These are the main reasons why we are witnessing a new era in the management of power. The colossal size and the grave importance of the power systems necessitate the need for the automation of energy management processes. This is where AI-aided technological solutions step in.
As evidenced by the work presented in this paper, the surge of variable renewables and distributed energy sources is accompanied by the surge of companies providing various AI solutions that aim to help solve the complex issues of the new power system. There are currently several major fields of AI application in the power sector. In this research, we have divided them into asset optimisation, system-level optimisation, data analytics and forecasts, predictive maintenance, e-mobility, customer support, cybersecurity and financial services. During our research, several hundred companies have been analysed. This resulted in a list of 220 AI-based companies operating in the power sector. To this date, and to the best of our knowledge, the research presented in the paper is the most comprehensive review of the actual, business-based application of AI in the power sector.
Research presented in the paper shows that the growing researcher interest in applying AI-based techniques in the power sector is accompanied by a growing number of companies following suit in real business applications. These companies are trying to implement numerous innovative solutions to all segments of the energy supply chain. In recent years, start-ups have been developing into successful companies. Many successful start-ups and companies have been acquired by incumbent companies in the sector. In such a way, incumbents combine their strengths with the agility of the start-up they acquired. Despite the lack of agility oftentimes seen in companies of the power sector, we are witnessing a new age in which the sector is becoming increasingly open towards newentrants and disruptors. This process is facilitated by structural changes in the power supply chain.
Business reports regarding other business sectors reveal that AI-based applications are most commonly used to support different types of service optimisations. In other words, they are focused on customers. Traditionally, power companies have always been focused on the process of providing a reliable source of energy. While other sectors, such as technology, media, and telecom (TMT), have shown more agility and switched to being customer-centric, most power companies have continued to maintain their traditional business model. This is the reason why many of them are failing to provide satisfactory financial returns—because they are still focused on providing a commodity, not a service. Research presented in this paper confirms that in the advent of the power sector′s digital age, the majority of digitally-equipped newentrants are still focused on augmenting the efficiency of the process rather than the service.
To provide a final conclusion, given the new energy paradigm driven by distributed energy sources, bidirectional flows and variable RES, it is essential to equip the distribution network with sensors that enable the application of data-driven AI techniques. AI will certainly play a major part in the future of the power sector. However, the successful implementation of these new solutions will take a considerable amount of time and significant effort. To follow the trends set by other industries, the power sector will have to shift its current focus from providing commodities to achieving an open innovation, customer-centric business model. Research presented in the paper shows that major steps have been taken, but further progress in this direction is yet to be made.

Author Contributions

Conceptualization, V.F. and A.V.; literature review, V.F. and D.M.; investigation and data acquisition V.F. and D.M.; paper writing and editing, V.F.; Supervision, A.V. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Data Availability Statement

Data obtained from numerous online sources.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1 lists 220 AI companies in the power sector. It categorizes them by name, country of origin, target market and field of interest according to Figure A1.
Figure A1. Explanation of Table A1.
Figure A1. Explanation of Table A1.
Energies 16 01077 g0a1
Table A1. 220 AI companies in the power sector in 2022. (websites last accessed on 5 January 2023).
Table A1. 220 AI companies in the power sector in 2022. (websites last accessed on 5 January 2023).
Aids solar power plant design, scans sites to establish solar potential
Optimizes low carbon heating and cooling needs of buildings
Carrier grade IoT connectivity platform
Cloud-based network analytics platform enables distribution system operators to better understand, operate, and plan their power grid infrastructure using data-driven network analytics
Developer and integrator of solutions for PV asset management and O&M
Ambyint developed an automation and analytics solution for artificial lift optimization that builds on traditional physics-based techniques
Creates personalized digital experiences for customers
Advanced hydrodynamic analysis, dynamic cable analysis and static cable stability as well as a range of solutions regarding RES
Provides AI-based smart meter analytics for utilities
Provides predictive maintenance solutions for renewable energy assets such as wind turbines, solar farms, and hydropower plants
Real-time monitoring and control of buildings
12Ari analyticsChina
Solar power plant production forecast and optimization, data-driven EE optimization of buildings
13Arloid AutomationUK
Helps cut energy costs with a solution that automatically adjusts HVAC settings in buildings based on changing environmental conditions
14Arundo AnalyticsUSA
The company uses data and predictive solutions to reduce maintenance costs, improve revenue, and avoid outages for industrial companies
15Aurora SolarCanada
Develops cloud-based software that aids solar PV engineering design
AutoGrid integrates all distributed energy resources by the use of flexibility management
EE for industrial cooling and HVAC
18Beijing Rongxing TechnologyChina
Develops predictive maintenance solutions, and integrated EMS platforms
19Beyond LimitsUSA
Beyond Limits is an Industrial and Enterprise grade AI technology company that covers the full range of Artificial Intelligence capabilities
Customer service platform provider, energy analytics for distribution system management related programs
21Blink Energy Inc.USA
Develops a platform that provides utilities with real-time data collection, fault indication, and helps prevents ice on power lines
Blue Wave AI Labs is using predictive analytics to operate nuclear reactors across the United States and to help them operate as safely and efficiently as possible
BluWave-ai uses artificial intelligence to improve operations for energy grids and renewable energy sources such as wind and solar farms
Energy optimization for smart grids and fleet electrification
Offers electric vehicle charging management solutions
26BrainBox AICanada
Developed a fully autonomous commercial HVAC solution, BrainBox AI, that uses predictive and self-adapting AI to optimise a building’s HVAC system for maximum energy reduction
Analytics and optimization solutions for building management
28Buzz SolutionsUSA
Buzz Solutions is developing artificial intelligence technology to spot and analyze power line flaws so companies can repair them before a fire starts
Uses machine learning techniques to enable accurate forecasting, benchmarking, building optimization, demand response, and anomaly detection. The company’s digital platform for utilities offers application for performance optimization for gas power plants, efficiency optimization of heating and cooling plants, energy analysis and management for enterprise and consumer customers, and anomaly detection of wind turbines.
30Carbon RelayUSA
Carbon Relay tackles data centre cooling with AI
Centrica’s REstore provides cloud-based demand side management. The technology is currently used by more than 150 industrial energy consumers in Europe.
Kisensum develops software to control and optimize energy resources, including photovoltaics, energy storage and charging stations for electric vehicles
Data-driven building operations software
Optimizing second life and recycling of batteries
Develops autonomous drones for thermal imaging and cleaning of solar panels and glass surfaces
The largest provider of emission-reducing energy solutions across North America
Building EMS
Delivers weather insights helping to manage weather related challenges
Meteorological data analysis
Provides drone-based wind turbine inspection and monitoring repair and maintenance
41COI Energy ServicesUSA
COI Energy Solutions deals with energy waste issues through its EMS platform improving energy performance of buildings
42Coulomb AIUSA
Optimizes batteries’ operation
Provides data, automation, and collaborative tools to design better buildings
The company’s behind-the-meter load approach enables alternative revenue streams for renewable and clean energy projects
cyberGRID offers innovative ICT-based flexibility management technology, integration of renewable energies and storage devices
DABBEL controls the energy system in buildings reducing energy consumption
Develops AI-driven sustainable technologies which enable people to leverage solar energy to power their cars
Detects anomalies and anticipates incidents, and uses data to predict future requirements
49DCSix Technologies Ireland
Provides an energy monitoring platform that help reduce energy consumption
Google’s DeepMind is the world leader in artificial intelligence research and its application in different fields, such as games, medicine, energy efficiency.
51DeepVolt Tunisia
AI-powered software for the placement and sizing of electric vehicle charging stations
52Detect TechnologiesIndia
Identifies fault differences, workforce safety hazards, monitors process automation and possible risks, generates analytics on data consumption
53Dexter EnergyNetherlandshttps://dexterenergy.nlC/E
Dexter provides forecasting and dispatching solutions based on AI and cloud-based technology that increases efficiency and reduces cost
Customer service platform provider, energy analytics
Develops autonomous driving systems and smart mobility solutions
56Ecolibrium EnergyIndia
The company’s platform provides a holistic view of buildings’ energy profile, develops customer engagement systems
Provides analyses of buildings’ energy performance and optimizes energy retrofit of buildings
Develops electric hybrid systems for electrification of transport and electric powertrains
Develops solar energy monitoring solutions
60Elevate North Macedonia
Develops autonomous energy forecasting solutions for utilities
Develops AI-driven smart grid solutions
Develops IoT-enabled battery management systems
Forecasts energy production
Provides a platform for enterprises to optimize their EE
65Energy PoolFrance
Builds and operates demand-side management solutions
66Energy XSouth Korea
Developed an AI-driven platform that allows corporate and individual users to invest in renewable energy projects worldwide
Developed a platform offering HVAC management solutions for commercial applications
Developed a platform optimizes homes, buildings and electric vehicle charging stations
Develops solutions to help with fault forecasting, identification and localization, life-time estimation, energy loss estimation and grid health assessment
Develops software solutions for power generation forecasting, optimizes district heating systems
Facilitates companies to invest in energy efficiency solutions
EMS and trading
73EQuota EnergyChina
AI & Big Data supported EMS Service provider
Analytics solutions and emissions reporting to empower corporate fleet electrification
Develops e-mobility solutions
76Evolve EnergyUSA
Developed the Kraken platform for customer management now licensed to E.ON, Npower, Origin Energy and EDF Energy and providing support to millions of customers. The company is also focused on providing flexibility and market access.
Analytics solutions for building management
Largest demand response aggregator in UK
Developed a platform Cerebra focused on improving asset uptime and operational efficiency
80Foghorn SystemsUSA
FogHorn is an IoT platform that provides a complete edge solution consisting of a highly miniaturized complex event processing engine that derives real-time insights
81Fresh EnergyGermany
Develops online building EMS solutions
Remote sensing, forecasting, data capture, and reporting for solar and wind power
83Future GridAustralia
Real-time smart meter data analytics
Provides forecasting software for energy storage systems and empowers customers to achieve bid and trading optimization for their power assets
Optimizes battery charging solutions for electric vehicles
86Generac Grid ServicesCanada
Manages grid assets and balance supply and demand in real time
Develops self-service solutions
88Gilytics AGSwitzerland
Automates infrastructure planning, routing, and monitoring
89Glint SolarNorway
Identifies and analyses optimal solar sites using satellite imagery
90Green RunningUK
Predictive maintenance technology for smart, sustainable appliances through Verv, a home energy assistant which monitors energy use in a home to help cut costs and save energy
Big Data integration for utilities
Develops an energy management system and visualization software
Operates private and public vehicle-to-grid charging infrastructure
94Grid AIJapan
Grid optimization
Uses AI-powered technology to optimize system operation to facilitate savings and manage price volatility
AI driven fully automated technology takes care of day-to-day EMS decisions
The company’s data platform automatically aggregates energy interval data, billing data, weather history data, and weather forecast data to power a rich set of analytic services
Hank is enabling service providers and building owners to proactively manage HVAC costs and improve EE of commercial buildings
99Hive PowerSwitzerland
The company’s platform monitors resource distribution and analyzes the usage behavior of consumers (from energy communities to EV charging), it also provides tools to optimize energy trading and prevent grid overloads
Delivers a platform that connects sensors and provides real-time data and performs analysis of heating settings and energy consumption
Provides home automation solutions for residential buildings
Automate customer service
103IND Technology Australia
IND.T is uses DA and smart sensing to reduce unplanned outages
Develops energy related services using Big Data and machine learning in order to improve EE
105Infra SolarNetherlands
Focuses on digitizing electricity consumption, optimising electric mobility, and energy consumption management
Innowatts’ platform transforms how energy providers understand and serve their customers. It uses AI to unlock meter-level data, provide consumer insights and make business processes automated and smarter for energy retailers, utilities and grid operators.
Solar power analytics
Lleak detection and video analytics systems for surveillance
Invenia’s Energy Intelligence System is a cloud-based machine learning platform that uses big, high frequency data to solve complex problems in real time. Invenia currently applies its platform to optimize electric utility operations as well as electricity markets themselves.
Forecasting and pattern recognition to manage and optimize operations
111Ion Energy LabsIndia
Develops battery management and mobility systems
Real-time monitoring and control of the electricity grid-edge
Itron’s portfolio of smart networks, software, services, meters and sensors helps our customers better manage energy and water
114Jungle AIPortugal
AI-based Canopy aims to increase production and prevent unplanned downtime and has industrial and power generation applications
115Kagera AISerbia
Optimizes production and minimizes downtime
The company provides energy optimisation and demand response services for buildings by connecting to buildings through building management systems and adjusting HVAC energy consumption in real time
Kayrros is an advanced DA company that helps global energy market participants make better investment decisions
118Kiwi Power (Engie)UK
Provides asset optimisation using flexibility of generation capacity to earn revenue by accessing wholesale market opportunities and participating in grid demand-side services
AI based search and analytics
Leanheat aims to use artificial intelligence to improve climate control in multi-family buildings
Providing smart maintenance for wind power plants
Home EMS system that links high numbers of domestic battery systems to a suite of advanced algorithmic controls. By coordinating the actions of these batteries Levelise can provide balancing services to the electricity transmission operator and to suppliers
The company’s platform offers home automation services
Platform that delivers flexible energy in real time by an aggregation of flexible energy generation and storage assets.
Lition and Watty provide a power usage monitoring system for commercial or personal purposes.
Provider of satellite-based power grid monitoring services
Data monitoring and analysis for solar power plants
Develops an EMS and monitoring platform
MAC created GridWatch - low-cost earth fault monitoring product solutions based on cloud back-end solution IOT
130Mandulis EnergyUganda
Develops and operates renewable energy projects focused on the sustainable biomass sector
They are a DA company focusing on weather prediction and how it impacts the energy commodity market. The company uses machine learning and big data to create self-learning algorithms that produce accurate predictions when it comes to weather and its impact on energy supply. The algorithm adapts and adjusts to the data that comes in to predict future behaviour as well.
Metron’s platform provides insight into energy usage and provides data on energy intelligence strategies
Customer service, demand forecast, optimization of energy production and scheduling, defect detection
Provides a location intelligence platform to identify EV charging hotspots
Moduly helps residential and commercial electricity users optimize their EE by allowing them to shift consumption peak hours, and reduce energy consumption
Focuses on smart battery systems and smart charging, and platform that allows households to minimize energy bills by optimizing battery and electric vehicle performance
137Morgan Solar Inc.Canada
Solar power plant production forecast and optimization, data-driven EE optimization of buildings
138Myst AIUSA
Myst AI is a developer of the AI-based data analysis platform intended for electricity demand and supply forecasting
139NanoLock Security USA
NanoLock device-level protection and management secures IoT and connected devices
Focuses on plant failure prediction systems and building EMS systems
141Nest LabsUSA
Nest Labs is a home automation company manufacturing sensor-driven, Wi-Fi-enabled, self-learning thermostats and smoke detectors
142Neurons LabUkrainehttps://www.neurons-lab.comC
Neurons Lab specializes in designing AI-based solutions to maximize energy outputs for small businesses and start-ups by forecasting renewable energy production (PV, Wind) and predicting energy consumption
143Nnergix Spain
Manages renewable energy and optimizes spinning reserves by AI-based algorithms and analytic models. Incorporates artificial intelligence to improve the accuracy of renewable energy forecasting
144Notilo PlusFrance
Provides underwater analysis
Nrgi is developing an AI-based B2B energy marketplace with energy price benchmarks, using an AI-based forecasting engine
146Octopus EnergyUK
Developed a cloud-based smart grid platform that balances loads around the grid
147Ogre AIRomania
Ogre AI develops automated B2B platform which uses machine learning to offer financial and operational decision-making support to energy and utilities companies
148Open EnergiUK
The company’s platform, Dynamic Demand 2.0, automatically optimizes and trades power from low carbon technologies such as battery storage, EVs, demand-side response, hydrogen electrolysers and smart buildings
149Origami EnergyUK
Origami’s independent energy data platform enables real-time market access, physical control, scheduling & dispatch, contract & position management, and price & volume forecasting
Orison offers residential and commercial self-installable energy storage systems paired with flexible fleet control tools that enable electric utilities and energy retailers to improve grid resilience and manage peak loads
Osperity’s technology provides AI-driven intelligent visual monitoring for industrial operations that can result in improved safety, reduced carbon footprints, etc.
152Palmetto Clean TechnologyUSA
Insights and design for rooftop solar installation, monitoring and predictive maintenance
Operates a cloud-based platform and integrated service that supports the end-to-end process of procuring and operating flexibility
154Ping ServicesAustralia
Ping records changes in this signature to continuously monitor the health of wind turbine blades and use advanced acoustic analysis to detect damage
155Ping ThingsUSA
The company uses big data software and science to detect events on the power grid. The platform records data from internal and external sources to utilities. This helps predict if an event, such as asset failure, will occur and alert the control room so they can avoid any outages.
Provides operational steps for EV charging station development, and simulates the electric grid to provide energy DA
Aggregates smaller producers and consumers
The company’s platform offers advanced DA, enhances the performance of renewable energy or energy storage assets, streamlines operations and maintenance
Optimizes operation of generation assets
Quadrical develops AI-based solar plant monitoring and forecasting platform
Developed a platform for infrastructure monitoring, optimization of energy consumption, and analytics
R8 Digital Operator developed add-on software for existing building automation systems
Designs, develops and operates service-oriented solutions that enhance utility performance and consumer EMS
164Raptor MapsUSA
Advanced analytics and aerial thermal inspections for solar power plants
165Rated PowerSpain
Their technology finds the smartest way to maximize the value of photovoltaic plants, scanning millions of iterations, finding the best configuration and immediately generating hundreds of pages of engineering documents
Raycatch developed DeepSolar, a digital asset management system that automates and optimizes solar PV assets
Works as an aggregator connecting homes to one community where clean energy is produced, stored and distributed among a network of people
Increases longevity of batteries
Rhythmos assembles critical information from legacy systems to automatically identify, characterize, quantify and forecast EV charging needs and host utility grid constraints
170Sawatch Labs USA
EV analytics including tailpipe emissions calculations, vehicle specific EV recommendations, site specific supply equipment, energy demand projections, and operational analysis of EV
171Schneider ElectricUSA
AutoGrid Systems by Schneider Electric organizes energy data and employs big DA to generate real-time predictions, and offers flexibility solutions
Predictive maintenance for power lines, and solar and wind power plants
173Senfal (Vattenfall)Netherlands
Senfal is a Dutch start-up that offers innovative software services to large industrial customers, wind and solar farms, as well as battery owners for unlocking value from flexibility
Optimizes building management
175Simerse AIUK
Helping electric utilities find defects on critical utility equipment
Skyqraft conducts aerial inspection of power lines through unmanned airplanes and machine learning
Drone-aided predictive maintenance
SmartCat develops software used to optimize heating and cooling devices, and to reduce electricity consumption
An end-to-end real-time analytics platform for solar plants
Provides a cloud-based monitoring solution for wind farm assets
Sobolt operates an AI solution HeatPuls that maps heat losses in buildings
182Social EnergyUK
Creates solutions flexibility and demand response for homeowners and businesses
183Solar AISingapore
Aids solar power plant desing, scans sites to establish solar potential
184Solar CaptusAustralia
Their platforms aid designing rooftop solar units, and facilitate digital marketing
185Solar Inspectron AIGreece
Drone thermal inspections of PV assets
Autonomous solar panel cleaning
SOLshare created a peer-to-peer energy exchange network of rural households and small businesses with rooftop solar home systems
The company operates a platform that helps integrate community-based solar power
Provides monitoring solutions of large-scale rooftop and outdoor systems and develops comprehensive databases for the provision of device benchmarks and analysis
Developed cyber-physical software for the safety, security, and reliability
The company’s platform Athena forms virtual power plants to maximize the value of energy resources through a combination of machine learning and predictive analytics
Designed a platform that manages infrastructure inspections through image recognition
Optimizes battery storage
Develops V2H and V2B charging systems that ensure the availability of energy in EVs during the day and feeds back the excess to households at night
The company developed a platform NEURAL that gives operational guidelines to O&M teams
Develops models that perform energy forecasts in photovoltaic power plants and applies AI and ML to estimate soiling in photovoltaic modules
197Sync EnergyUSA
Sync develops no-code AI-based predictive simulations and analytics, that streamline planning and operations for electrical utilities.
198The Solar LabsIndiahttp://thesolarlabs.comC/E
Develops software that aids solar PV engineering design
Enables end users to manage their energy assets more efficiently
Provides an all-in retrofit solution that transforms electrical space and water heaters into energy-efficient storage devices
Tibber is a digital electricity supplier that uses AI to regulate power for houses based on their predicted levels of consumption
tiko’s Virtual Power Plant is designed to stabilize the grid and reduce these fluctuations
Develops algorithms for energy forecasting
Develops energy forecast models
Provider of digital twin-based battery DA and management platform
206Unleash liveAustralia
Video analytics for predictive maintenance
207Upside EnergyUK
Software platform for managing, controlling and optimising all distributed energy resources
Urbint predicts threats to workers and critical infrastructure to stop incidents before they happen
209Verdigris TechnologiesUSA
Verdigris Technologies is a SaaS-based platform that develops artificial intelligence in order to optimize energy consumption
Predicts energy demand, grid power flows, possible outages, and renewable energy production by analysing data collected by smart meters, drones, and sensors
211Virtual Power SolutionsUK
The company’s platforms focus on smart homes and smart cities. EMS system that combines demand management with building automation technology targeted for the business sector
Customer service automation with AI bots
Technology integrates data from drones, IoT sensors, and offline sources in predictive maintenance and asset management
Demand response for home heating
Offers hardware-as-a-service that transforms lampposts into smart electric vehicle charging stations
Greensmith Energy (Wärtsilä) uses software-based intelligence and machine learning to enhance grid systems and networks
Offers a home EMS platform
Willow empowers asset owners and operators to make proactive, data-led decisions in real time
The company provides solar leases to customers of any business that installs or sells solar panels
Worlds’ technology combines deep learning and IoT inside of a 4D environment to give organizations the ability to observe their organization’s physical space and then, analyze and learn from their physical surroundings


  1. Vial, G. Understanding digital transformation: A review and a research agenda. J. Strateg. Inf. Syst. 2019, 28, 118–144. [Google Scholar] [CrossRef]
  2. Balsmeier, B.; Woerter, M. Is this time different? How digitalization influences job creation and destruction. Res. Policy 2019, 48, 103765. [Google Scholar] [CrossRef]
  3. Saarikko, T.; Westergren, U.H.; Blomquist, T. Digital transformation: Five recommendations for the digitally conscious firm. Bus. Horiz. 2020, 63, 825–839. [Google Scholar] [CrossRef]
  4. Bomparda, E.; Botterud, A.; Corgnatia, S.; Huanga, T.; Jafaric, M.; Leone, P.; Mauro, S.; Montesano, G.; Papa, C.; Profumo, F. An electricity triangle for energy transition: Application to Italy. Appl. Energy 2020, 227, 115525. [Google Scholar] [CrossRef]
  5. Joskow, P.L. Lessons Learned from Electricity Market Liberalization. Energy J. 2008, 29, 9–42. [Google Scholar] [CrossRef]
  6. Višković, A.; Šimunić, D.; Franki, V. Innovation platfirm—A novel energy service utility. In Proceedings of the 43rd International Convention on Information, Communication and Electronic Technology, MIPRO 2020, Opatija, Croatia, 28 September–2 October 2020; pp. 425–430. [Google Scholar]
  7. Višković, A.; Franki, V. Evaluating and forecasting direct carbon emissions of electricity production: A case study for South East Europe. Energy Sources Part B Econ. Plan. Policy 2022, 17, 2037028. [Google Scholar] [CrossRef]
  8. Verhoef, P.C.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Qi Dong, J.; Fabian, N.; Haenlein, M. Digital transformation: A multidisciplinary reflection and research agenda. J. Bus. Res. 2021, 122, 889–901. [Google Scholar] [CrossRef]
  9. Loock, M. Unlocking the value of digitalization for the European energy transition: A typology of innovative business models. Energy Res. Soc. Sci. 2020, 69, 101740. [Google Scholar] [CrossRef]
  10. Di Silvestre, M.L.; Favuzza, S.; Riva Sanseverino, E.; Zizzo, G. How Decarbonization, Digitalization and Decentralization are changing key power infrastructures. Renew. Sustain. Energy Rev. 2018, 93, 483–498. [Google Scholar] [CrossRef]
  11. Višković, A.; Franki, V.; Damiano, A. Integrated strategic action for facilitating the electrification process. In Proceedings of the 44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021, Opatija, Croatia, 27 September–1 October 2021; pp. 551–557. [Google Scholar]
  12. Boza, P.; Evgeniou, T. Artificial intelligence to support the integration of variable renewable energy sources to the power system. Appl. Energy 2021, 290, 116754. [Google Scholar] [CrossRef]
  13. Duan, Y.; Edwards, J.S.; Dwivedi, Y.K. Artificial intelligence for decision making in the era of Big Data—Evolution, challenges and research agenda. Int. J. Inf. Manag. 2019, 48, 63–71. [Google Scholar] [CrossRef]
  14. Višković, A.; Franki, V.; Bašić-šiško, A. City-Level Transition to Low-Carbon Economy. Energies 2022, 15, 1737. [Google Scholar] [CrossRef]
  15. Ramos, C.; Liu, C. AI in Power Systems and Energy Markets. IEEE Intell. Syst. 2011, 26, 5–8. [Google Scholar] [CrossRef]
  16. Ali, S.S.; Choi, B.J. State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review. Electronics 2020, 9, 1030. [Google Scholar] [CrossRef]
  17. Višković, A.; Franki, V.; Jevtić, D. Artificial intelligence as a facilitator of the energy transition. In Proceedings of the 45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022, Opatija, Croatia, 23–27 May 2022; pp. 494–499. [Google Scholar]
  18. Ahmad, T.; Zhu, H.; Zhang, D.; Tariq, R.; Bassam, A.; Ullah, F.; Ghamdi, A.S.; Alshamrani, S.S. Energetics Systems and artificial intelligence: Applications of industry 4.0. Energy Rep. 2022, 8, 334–361. [Google Scholar] [CrossRef]
  19. Ahmad, T.; Zhang, D.; Huang, C.; Zhang, H.; Dai, N.; Song, Y.; Chen, H. Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. J. Clean. Prod. 2021, 289, 125834. [Google Scholar] [CrossRef]
  20. Kumbhar, A.; Dhawale, P.G.; Kumbhar, S.; Patil, U.; Magdum, P. A comprehensive review: Machine learning and its application in integrated power system. Energy Rep. 2021, 7, 5467–5474. [Google Scholar] [CrossRef]
  21. Antonopoulos, I.; Robu, V.; Couraud, B.; Kirli, D.; Norbu, S.; Kiprakis, A.; Flynn, D.; Elizondo-Gonzalez, S.; Wattam, S. Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. Renew. Sustain. Energy Rev. 2020, 130, 109899. [Google Scholar] [CrossRef]
  22. Zhang, Y.; Shi, X.; Zhang, H.; Cao, H.; Terzija, V. Review on deep learning applications in frequency analysis and control of modern power system. Int. J. Electr. Power Energy Syst. 2022, 136, 107744. [Google Scholar] [CrossRef]
  23. Hou, H.; Liu, C.; Wang, Q.; Wu, X.; Tang, J.; Shi, Y.; Xie, C. Review of load forecasting based on artificial intelligence methodologies, models, and challenges. Electr. Power Syst. Res. 2022, 210, 108067. [Google Scholar] [CrossRef]
  24. Yazici, I.; Beyca, O.F.; Delen, D. Deep-learning-based short-term electricity load forecasting: A real case application. Eng. Appl. Artif. Intell. 2022, 109, 104645. [Google Scholar] [CrossRef]
  25. Mazzeo, D.; Herdem, M.S.; Matera, N.; Bonini, M.; Wen, J.Z.; Nathwani, J.; Oliveti, G. Artificial intelligence application for the performance prediction of a clean energy community. Energy 2021, 232, 120999. [Google Scholar] [CrossRef]
  26. Weng Kow, K.; Wan Wong, Y.; Kumar Rajkumar, R.; Kumar Rajkumar, R. A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events. Renew. Sustain. Energy Rev. 2016, 56, 334–346. [Google Scholar]
  27. Mellit, A.; Kalogirou, S.A. Artificial intelligence techniques for photovoltaic applications: A review. Prog. Energy Combust. Sci. 2008, 34, 574–632. [Google Scholar] [CrossRef]
  28. Jha, S.K.; Bilalovic, J.; Jha, A.; Patel, N.; Zhang, H. Renewable energy: Present research and future scope of Artificial Intelligence. Renew. Sustain. Energy Rev. 2017, 77, 297–317. [Google Scholar] [CrossRef]
  29. Richter, L.; Lehna, M.; Marchand, S.; Scholz, C.; Dreher, A.; Klaiber, S.; Lenk, S. Artificial Intelligence for Electricity Supply Chain automation. Renew. Sustain. Energy Rev. 2022, 163, 112459. [Google Scholar] [CrossRef]
  30. Aguilar, J.; Garces-Jimenez, A.; R.-Moreno, M.D.; García, R. A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings. Renew. Sustain. Energy Rev. 2021, 151, 111530. [Google Scholar] [CrossRef]
  31. Aziz, A.F.A.; Khalid, S.N.; Mustafa, M.W.; Shareef, H.; Aliyu, G. Artificial Intelligent Meter development based on Advanced Metering Infrastructure technology. Renew. Sustain. Energy Rev. 2013, 27, 191–197. [Google Scholar] [CrossRef]
  32. Clarke, R. Regulatory alternatives for AI. Comput. Law Secur. Rev. 2019, 35, 398–409. [Google Scholar] [CrossRef]
  33. Roh, Y.; Heo, G.; Whang, S.E. A Survey on Data Collection for Machine Learning: A Big Data—AI Integration Perspective. IEEE Trans. Knowl. Data Eng. 2021, 33, 1328–1347. [Google Scholar] [CrossRef][Green Version]
  34. Alimi, O.A.; Ouahada, K.; Abu-Mahfouz, A.M. A Review of Machine Learning Approaches to Power System Security and Stability. IEEE Access 2020, 8, 113512–113531. [Google Scholar] [CrossRef]
  35. Lu, Z.; Lu, X.; Wang, W.; Wang, C. Review and evaluation of security threats on the communication networks in the smart grid. In Proceedings of the IEEE Military Communication Conference, San Jose, CA, USA, 31 October–3 November 2010. [Google Scholar]
  36. Küfeoğlu, S.; Liu, G.; Anaya, K.; Pollitt, M.G. Digitalisation and New Business Models in Energy Sector; Energy Policy Research Group, University of Cambridge: Cambridge, UK, 2019. [Google Scholar]
  37. Available online: (accessed on 5 January 2023).
  38. Available online: (accessed on 5 January 2023).
  39. Available online: (accessed on 5 January 2023).
  40. Available online: (accessed on 5 January 2023).
  41. Available online: (accessed on 5 January 2023).
  42. Russell, S.; Norvig, P. Artificial Intelligence: A Modern Approach, 4th ed.; Pearson: Boston, MA, USA, 2020. [Google Scholar]
  43. Haenlein, M.; Kaplan, H. A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. Calif. Manag. Rev. 2019, 61, 5–14. [Google Scholar] [CrossRef]
  44. Turing, A.M. Computing machinery and intelligence. Mind 1950, 59, 433–460. [Google Scholar] [CrossRef]
  45. Garnelo, M.; Shanahan, M. Reconciling deep learning with symbolic artificial intelligence: Representing objects and relations. Curr. Opin. Behav. Sci. 2019, 29, 17–23. [Google Scholar] [CrossRef]
  46. Newell, A.; Simon, H.A. Computer science as empirical inquiry: Symbols and search. Commun. ACM 1976, 19, 113–126. [Google Scholar] [CrossRef][Green Version]
  47. Milić, S.D.; Đurović, V.; Stojanović, M.D. Data science and machine learning in the IIoT concepts of power plants. Int. J. Electr. Power Energy Syst. 2023, 145, 108711. [Google Scholar] [CrossRef]
  48. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning Data Mining, Inference, and Prediction, 2nd ed.; Springer: Berlin/Heidelberg, Germany, 2008. [Google Scholar]
  49. Jin, X.-B.; Zheng, W.-Z.; Kong, J.-L.; Wang, X.-Y.; Bai, Y.-T.; Su, T.-L.; Lin, S. Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization. Energies 2021, 14, 1596. [Google Scholar] [CrossRef]
  50. Nikolaidis, P.; Chatzis, S. Gaussian process-based Bayesian optimization for data-driven unit commitment. Int. J. Electr. Power Energy Syst. 2021, 130, 106930. [Google Scholar] [CrossRef]
  51. Chen, Q.; Folly, K.A. Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review. Energies 2023, 16, 146. [Google Scholar] [CrossRef]
  52. Hassan, M.M.; Hassan, M.R.; de Albuquerque, V.H.C.; Pedrycz, W. Soft Computing for Intelligent Edge Computing. Appl. Soft Comput. 2022, 128, 109628. [Google Scholar] [CrossRef]
  53. Ibrahim, D. An Overview of Soft Computing. Procedia Comput. Sci. 2016, 102, 34–38. [Google Scholar] [CrossRef]
  54. Amit, K. Artificial Intelligence and Soft Computing—Behavioral and Cognitive Modeling of the Human Brain; CRC Press: Boca Raton, FL, USA, 2000. [Google Scholar]
  55. Mehdi, H.M.; Azeem, M.K.; Ahmad, I. Artificial intelligence based nonlinear control of hybrid DC microgrid for dynamic stability and bidirectional power flow. J. Energy Storage 2023, 58, 106333. [Google Scholar] [CrossRef]
  56. Minker, J. (Ed.) Logic-Based Artificial Intelligence; The Springer International Series in Engineering and Computer Science; Springer: Boston, MA, USA, 2000. [Google Scholar]
  57. Liu, Z.; Gao, Y.; Liu, B. An artificial intelligence-based electric multiple units using a smart power grid system. Energy Rep. 2022, 8, 13376–13388. [Google Scholar] [CrossRef]
  58. Schmoldt, D.L.; Rausche, H.M. Knowledge-Based Systems in Artificial Intelligence; McGraw-Hill Series in Forest Resources; McGraw-Hill: New York, NY, USA, 1989. [Google Scholar]
  59. Jain, P.; Saxena, A. A Multi-Agent based simulator for strategic bidding in day-ahead energy market. Sustain. Energy Grids Netw. 2023, 33, 100979. [Google Scholar] [CrossRef]
  60. McArthur, S.D.; Davidson, E.M.; Catterson, V.M.; Dimeas, A.L.; Hatziargyriou, N.D.; Ponci, F.; Funabashi, T. Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems. IEEE Trans. Power Syst. 2007, 22, 1753–1759. [Google Scholar] [CrossRef][Green Version]
  61. Khan, Z.A.; Khalid, A.; Javaid, N.; Haseeb, A.; Saba, T.; Shafiq, M. Exploiting Nature-Inspired-Based Artificial Intelligence Techniques for Coordinated Day-Ahead Scheduling to Efficiently Manage Energy in Smart Grid. IEEE Access 2019, 7, 140102–140125. [Google Scholar] [CrossRef]
  62. Kuo, P.-H.; Huang, C.-J. A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting. Energies 2018, 11, 213. [Google Scholar] [CrossRef][Green Version]
  63. Park, D.C.; El-Sharkawi, M.A.; Marks, R.J.; Atlas, L.E.; Damborg, M.J. Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 1991, 6, 442–449. [Google Scholar] [CrossRef][Green Version]
  64. Sulaiman, S.M.; Jeyanthy, P.A.; Devaraj, D. Artificial neural network based day ahead load forecasting using Smart Meter data. In Proceedings of the Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), Bengaluru, India, 21–23 January 2016; pp. 1–6. [Google Scholar]
  65. Lee, M. An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data. World Pat. Inf. 2020, 63, 102002. [Google Scholar] [CrossRef]
  66. Matijašević, T.; Antić, T.; Capuder, T. A systematic review of machine learning applications in the operation of smart distribution systems. Energy Rep. 2022, 8, 12379–12407. [Google Scholar] [CrossRef]
  67. Murphy, K.P. Machine Learning: A Probabilistic Perspective; MIT Press: Cambridge, MA, USA, 2012. [Google Scholar]
  68. Marsland, S. Machine Learning—An Algorithmic Perspective, 1st ed.; Chapman and Hall/CRC: Boca Raton, FL, USA, 2009. [Google Scholar]
  69. Wang, H.; Lei, Z.; Zhang, X.; Zhou, B.; Peng, J. A review of deep learning for renewable energy forecasting. Energy Convers. Manag. 2019, 198, 111799. [Google Scholar] [CrossRef]
  70. Forootan, M.M.; Larki, I.; Zahedi, R.; Ahmadi, A. Machine Learning and Deep Learning in Energy Systems: A Review. Sustainability 2022, 14, 4832. [Google Scholar] [CrossRef]
  71. Jan, B.; Farman, H.; Khan, M.; Imran, M.; Ul Islam, M.; Ahmad, A.; Ali, S.; Jeon, G. Deep learning in big data Analytics: A comparative study. Comput. Electr. Eng. 2019, 75, 275–287. [Google Scholar] [CrossRef]
  72. Janiesch, C.; Zschech, P.; Heinrich, K. Machine learning and deep learning. Electron. Mark. 2021, 31, 685–695. [Google Scholar] [CrossRef]
  73. Gerbaulet, C.; von Hirschhausen, C.; Kemfert, C.; Lorenz, C.; Oeia, P.-Y. European electricity sector decarbonization under different levels of foresight. Renew. Energy 2019, 141, 973–987. [Google Scholar] [CrossRef]
  74. Teece, D.J. Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Res. Policy 2018, 47, 1367–1387. [Google Scholar] [CrossRef]
  75. Dwivedi, Y.K. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 2021, 57, 101994. [Google Scholar] [CrossRef]
  76. Jacobson, D.; Dickerman, L. Distributed Intelligence: A Critical Piece of the Microgrid Puzzle. Electr. J. 2019, 32, 10–13. [Google Scholar] [CrossRef]
  77. Aguilar Madrid, E.; Antonio, N. Short-Term Electricity Load Forecasting with Machine Learning. Information 2021, 12, 50. [Google Scholar] [CrossRef]
  78. Metaxiotis, K.; Kagiannas, A.; Askounis, D.; Psarras, J. Artificial intelligence in short term electric load forecasting: A state-of-the-art survey for the researcher. Energy Convers. Manag. 2003, 44, 1525–1534. [Google Scholar] [CrossRef]
  79. Raza, Q.M.; Khosravi, A. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 2015, 50, 1352–1372. [Google Scholar] [CrossRef]
  80. Ali, D.; Yohanna, M.; Puwu, M.I.; Garkida, B.M. Long-term load forecast modelling using a fuzzy logic approach. Pac. Sci. Rev. A Nat. Sci. Eng. 2016, 18, 123–127. [Google Scholar] [CrossRef][Green Version]
  81. Nalcaci, G.; Özmen, A.; Weber, G.W. Long-term load forecasting: Models based on MARS, ANN and LR methods. Cent. Eur. J. Oper. Res. 2019, 27, 1033–1049. [Google Scholar] [CrossRef]
  82. Ranjith Kumar, K.; Surya Kalavathi, M. Artificial intelligence based forecast models for predicting solar power generation. Mater. Today Proc. 2018, 5, 796–802. [Google Scholar] [CrossRef]
  83. Abubakar, M.; Che, Y.; Ivascu, L.; Almasoudi, F.M.; Jamil, I. Performance Analysis of Energy Production of Large-Scale Solar Plants Based on Artificial Intelligence (Machine Learning) Technique. Processes 2022, 10, 1843. [Google Scholar] [CrossRef]
  84. Kuzlu, M.; Cali, U.; Sharma, V.; Güler, Ö. Gaining Insight into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools. IEEE Access 2020, 8, 187814–187823. [Google Scholar] [CrossRef]
  85. Dumitru, C.-D.; Gligor, A. Daily Average Wind Energy Forecasting Using Artificial Neural Networks. Procedia Eng. 2017, 181, 829–836. [Google Scholar] [CrossRef]
  86. Jørgensen, K.L.; Shaker, H.L. Wind Power Forecasting Using Machine Learning: State of the Art, Trends and Challenges. In Proceedings of the IEEE 2020, 8th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 12–14 August 2020; pp. 44–50. [Google Scholar]
  87. Ahmadi, A.; Nabipour, M.; Mohammadi-Ivatloo, B.; Amani, A.M.; Rho, S.; Piran, M.J. Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms. IEEE Access 2020, 8, 151511–151522. [Google Scholar] [CrossRef]
  88. Zhang, Y.; Zhao, Y.; Shen, X.; Zhang, J. A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms. Appl. Energy 2022, 305, 117815. [Google Scholar] [CrossRef]
  89. Möllerström, E.; Gregory, S.; Sugathan, A. Improvement of AEP Predictions with Time for Swedish Wind Farms. Energies 2021, 14, 3475. [Google Scholar] [CrossRef]
  90. Khan, W.; Walker, S.; Zeiler, W. Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach. Energy 2022, 240, 122812. [Google Scholar] [CrossRef]
  91. Kolsi, L.; Al-Dahidi, S.; Kamel, S.; Aich, W.; Boubaker, S.; Ben Khedher, N. Prediction of Solar Energy Yield Based on Artificial Intelligence Techniques for the Ha’il Region, Saudi Arabia. Sustainability 2023, 15, 774. [Google Scholar] [CrossRef]
  92. Bouteska, A.; Hajek, P.; Fisher, B.; Zoynul Abedin, M. Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network. Res. Int. Bus. Financ. 2023, 64, 101863. [Google Scholar] [CrossRef]
  93. Lago, J.; de Ridder, F.; de Schutter, B. Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy 2018, 221, 386–405. [Google Scholar] [CrossRef]
  94. Lago, J.; Marcjasz, G.; de Schutter, B.; Weron, R. Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. Appl. Energy 2021, 293, 116983. [Google Scholar] [CrossRef]
  95. Contreras, J.; Espinola, R.; Nogales, F.J.; Conejo, A.J. ARIMA models to predict next-day electricity price. IEEE Trans. Power Syst. 2003, 18, 1014–1020. [Google Scholar] [CrossRef]
  96. Van Gompel, J.; Spina, D.; Develder, C. Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks. Energy 2023, 266, 126444. [Google Scholar] [CrossRef]
  97. Barrera, J.M.; Reina, A.; Maté, A.; Trujillo, J.C. Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data. Sustainability 2020, 12, 6915. [Google Scholar] [CrossRef]
  98. Yessef, M.; Bossoufi, B.; Taoussi, M.; Motahhir, S.; Lagrioui, A.; Chojaa, H.; Lee, S.; Kang, B.-G.; Abouhawwash, M. Improving the Maximum Power Extraction from Wind Turbines Using a Second-Generation CRONE Controller. Energies 2022, 15, 3644. [Google Scholar] [CrossRef]
  99. Majidi Nezhad, M.; Heydari, A.; Pirshayan, E.; Groppi, D.; Astiaso Garcia, D. A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method. Renew. Energy 2021, 179, 2198–2211. [Google Scholar] [CrossRef]
  100. Muhammad Ashraf, W.; Moeen Uddin, G.; Afroze Ahmad, H.; Ahmad Jamil, M.; Tariq, R.; Wakil Shahzad, M.; Dua, V. Artificial intelligence enabled efficient power generation and emissions reduction underpinning net-zero goal from the coal-based power plants. Energy Convers. Manag. 2022, 268, 116025. [Google Scholar] [CrossRef]
  101. Stefanidou-Voziki, P.; Cardoner-Valbuena, D.; Villafafila-Robles, R.; Dominguez-Garcia, J.L. Data analysis and management for optimal application of an advanced ML-based fault location algorithm for low voltage grids. Int. J. Electr. Power Energy Syst. 2022, 142 Pt A, 108303. [Google Scholar] [CrossRef]
  102. Korjani, S.; Facchini, A.; Mureddu, M.; Rubino, A.; Damiano, A. Battery management for energy communities—Economic evaluation of an artificial intelligence-led system. J. Clean. Prod. 2021, 314, 128017. [Google Scholar] [CrossRef]
  103. Lee, D.; Chen, L. Sustainable Air-Conditioning Systems Enabled by Artificial Intelligence: Research Status, Enterprise Patent Analysis, and Future Prospects. Sustainability 2022, 14, 7514. [Google Scholar] [CrossRef]
  104. Qin, H.; Yu, Y.; Li, T.; Liu, X.; Li, L. Energy-efficient heating control for nearly zero energy residential buildings with deep reinforcement learning. Energy 2023, 264, 126209. [Google Scholar] [CrossRef]
  105. Nutakki, M.; Mandava, S. Review on optimization techniques and role of Artificial Intelligence in home energy management systems. Eng. Appl. Artif. Intell. 2023, 119, 105721. [Google Scholar] [CrossRef]
  106. Di Santo, K.G.; Di Santo, S.G.; Machado Monaro, R.; Saidel, M.A. Active demand side management for households in smart grids using optimization and artificial intelligence. Measurement 2018, 115, 152–161. [Google Scholar] [CrossRef]
  107. Vázquez-Canteli, J.R.; Nagy, Z. Reinforcement learning for demand response: A review of algorithms and modeling techniques. Appl. Energy 2019, 235, 1072–1089. [Google Scholar] [CrossRef]
  108. Huang, X.; Hong, S.H.; Yu, M.; Ding, Y.; Jiang, J. Demand Response Management for Industrial Facilities: A Deep Reinforcement Learning Approach. IEEE Access 2019, 7, 82194–82205. [Google Scholar] [CrossRef]
  109. Aguilar, J.; Bordons, C.; Arce, A. Chance Constraints and Machine Learning integration for uncertainty management in Virtual Power Plants operating in simultaneous energy markets. Int. J. Electr. Power Energy Syst. 2021, 133, 107304. [Google Scholar] [CrossRef]
  110. Krishna, G.; Singh, R.; Gehlot, A.; Akram, S.V.; Priyadarshi, N.; Twala, B. Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations. Electronics 2022, 11, 2695. [Google Scholar] [CrossRef]
  111. Wagner, F.; Milojevic-Dupont, N.; Franken, L.; Zekar, A.; Thies, B.; Koch, N.; Creutzig, F. Using explainable machine learning to understand how urban form shapes sustainable mobility. Transp. Res. Part D Transp. Environ. 2022, 111, 103442. [Google Scholar] [CrossRef]
  112. Bhuiyan, E.A.; Hossain, M.Z.; Muyeen, S.M.; Fahim, S.R.; Sarker, S.K.; Das, S.K. Towards next generation virtual power plant: Technology review and frameworks. Renew. Sustain. Energy Rev. 2021, 150, 111358. [Google Scholar] [CrossRef]
  113. Xiao, J.; Kong, X.; Jin, Q.; You, H.; Cui, K.; Zhang, Y. Demand-Responsive Virtual Power Plant Optimization Scheduling Method Based on Competitive Bidding Equilibrium. Energy Procedia 2018, 152, 1158–1163. [Google Scholar] [CrossRef]
  114. MacDougall, P.; Kosek, A.M.; Bindner, M.; Deconinck, G. Applying machine learning techniques for forecasting flexibility of virtual power plants. In Proceedings of the IEEE Electrical Power and Energy Conference (EPEC), Ottawa, ON, Canada, 12–14 October 2016; pp. 1–6. [Google Scholar]
  115. Kruse, J.; Schäfer, B.; Witthaut, D. Secondary control activation analysed and predicted with explainable AI. Electr. Power Syst. Res. 2022, 212, f108489. [Google Scholar] [CrossRef]
  116. Wu, B.; Dhammika Widanage, W.; Yang, S.; Liu, X. Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems. Energy AI 2020, 1, 100016. [Google Scholar] [CrossRef]
  117. Barrett, D.H.; Haruna, A. Artificial intelligence and machine learning for targeted energy storage solutions. Curr. Opin. Electrochem. 2020, 21, 160–166. [Google Scholar] [CrossRef]
  118. Liu, Y.; Guo, B.; Zou, X.; Li, Y.; Shi, S. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Mater. 2020, 31, 434–450. [Google Scholar] [CrossRef]
  119. Ahmed, M.; Zheng, Y.; Amine, A.; Fathiannasab, H.; Chen, Z. The role of artificial intelligence in the mass adoption of electric vehicles. Joule 2021, 5, 2296–2322. [Google Scholar] [CrossRef]
  120. Frendo, O.; Graf, J.; Gaertner, N.; Stuckenschmidt, H. Data-driven smart charging for heterogeneous electric vehicle fleets. Energy AI 2020, 1, 100007. [Google Scholar] [CrossRef]
  121. Qureshi, K.N.; Alhudhaif, A.; Jeon, G. Electric-vehicle energy management and charging scheduling system in sustainable cities and society. Sustain. Cities Soc. 2021, 71, 102990. [Google Scholar] [CrossRef]
  122. Bangert, P. (Ed.) Machine Learning and Data Science in the Power Generation Industry—Best Practices, Tools, and Case Studies; Elsevier: Amsterdam, The Netherlands, 2021. [Google Scholar]
  123. Zhang, Y.; Huang, T.; Bompard, E.F. Big data analytics in smart grids: A review. Energy Inform. 2018, 1, 8. [Google Scholar] [CrossRef]
  124. Shin, W.; Han, J.; Rhee, W. AI-assistance for predictive maintenance of renewable energy systems. Energy 2021, 221, 119775. [Google Scholar] [CrossRef]
  125. De Benedetti, M.; Leonardi, F.; Messina, F.; Santoro, C.; Vasilakos, A. Anomaly detection and predictive maintenance for photovoltaic systems. Neurocomputing 2018, 310, 59–68. [Google Scholar] [CrossRef]
  126. Tian, Z.; Zhang, H. Wind farm predictive maintenance considering component level repairs and economic dependency. Renew. Energy 2022, 192, 495–506. [Google Scholar] [CrossRef]
  127. Cruz Garcia, M.; Sanz-Bobi, M.A.; Pico, J. SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox. Comput. Ind. 2006, 57, 552–568. [Google Scholar] [CrossRef]
  128. Alsheryani, R.M.; Alkaabi, S.S.; Aldhaheri, A.M.; Khouri, F.I.; Alharmoodi, S.I.; Shadid, T.T.; Alhajeri, A.S. Applying Artificial Intelligence (AI) for Predictive Maintenance of Power Distribution Networks: A Case Study of Al Ain Distribution Company. In Proceedings of the 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, United Arab Emirates, 19–21 November 2019; pp. 1–5. [Google Scholar]
  129. Mellit, A.; Kalogirou, S.A.; Hontoria, L.; Shaari, S. Artificial intelligence techniques for sizing photovoltaic systems: A review. Renew. Sustain. Energy Rev. 2009, 13, 406–419. [Google Scholar] [CrossRef]
  130. Zhao, Y.; Li, T.; Zhang, X.; Zhang, C. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renew. Sustain. Energy Rev. 2019, 109, 85–101. [Google Scholar] [CrossRef]
  131. Fahim, S.R.; Sarker, S.K.; Muyeen, S.M.; Das, S.K.; Kamwa, I. A deep learning based intelligent approach in detection and classification of transmission line faults. Int. J. Electr. Power Energy Syst. 2021, 133, 107102. [Google Scholar] [CrossRef]
  132. Boretti, A. Integration of solar thermal and photovoltaic, wind, and battery energy storage through AI in NEOM city. Energy AI 2021, 3, 100038. [Google Scholar] [CrossRef]
  133. Zekić-Sušac, M.; Mitrović, S.; Has, A. Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities. Int. J. Inf. Manag. 2021, 58, 102074. [Google Scholar] [CrossRef]
  134. Frankenberger, K.; Weiblen, T.; Gassmann, O. Network configuration, customer centricity, and performance of open business models: A solution provider perspective. Ind. Mark. Manag. 2013, 42, 671–682. [Google Scholar] [CrossRef]
  135. Višković, A.; Franki, V. Value co-creation through a digital platform business model in the power sector. In Proceedings of the 44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021, Opatija, Croatia, 27 September–1 October 2021; pp. 425–430. [Google Scholar]
  136. Sung, E.C.; Bae, S.; Han, D.-I.D.; Kwon, O. Consumer engagement via interactive artificial intelligence and mixed reality. Int. J. Inf. Manag. 2021, 60, 102382. [Google Scholar] [CrossRef]
  137. Servion Global Soutions. Artificial Intelligence Report. In Proceedings of the Smart Grid Conference (SGC), Tehran, Iran, 20–21 December 2017. [Google Scholar]
  138. Heinen, S.; Richards, P. Towards customer-centric energy utilities—A granular data-driven bottom-up approach to understanding energy customer trends. Electr. J. 2020, 33, 106836. [Google Scholar] [CrossRef]
  139. Libai, B.; Bart, Y.; Gensler, S.; Hofacker, C.F.; Kaplan, A.; Kötterheinrich, K.; Kroll, E.B. Brave New World? On AI and the Management of Customer Relationships. J. Interact. Mark. 2020, 51, 44–56. [Google Scholar] [CrossRef]
  140. Schweiger, G.; Eckerstorfer, L.V.; Hafner, I.; Fleischhacker, A.; Radl, J.; Glock, B.; Wastian, M.; Rößler, M.; Lettner, G.; Popper, N.; et al. Active consumer participation in smart energy systems. Energy Build. 2020, 227, 110359. [Google Scholar] [CrossRef]
  141. Jou, Y.-T.; Saflor, C.S.; Mariñas, K.A.; Young, M.N.; Prasetyo, Y.T.; Persada, S.F. Assessing Service Quality and Customer Satisfaction of Electric Utility Provider’s Online Payment System during the COVID-19 Pandemic: A Structural Modeling Approach. Electronics 2022, 11, 3646. [Google Scholar] [CrossRef]
  142. Nicolescu, L.; Tudorache, M.T. Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review. Electronics 2022, 11, 1579. [Google Scholar] [CrossRef]
  143. Cisco. Internet of Things (IoT) Data Continues to Explode Exponentially. Who is Using that Data and How? 2018. Available online: (accessed on 26 October 2021).
  144. Desjardins, J. How much data is generated in a day? World Economic Forum. 2019. Available online: (accessed on 26 October 2021).
  145. Statista. Internet of Things (IoT) Connected Devices Installed Base Worldwide from 2015 to 2025 (in Billions). Statista. 2018. Available online: (accessed on 26 October 2021).
  146. McKinsey. The State of AI in 2021; McKinsey: Chicago, IL, USA, 2021. [Google Scholar]
  147. Franki, V.; Višković, A. Multi-criteria decision support: A case study of Southeast Europe power systems. Util. Policy 2021, 73, 101286. [Google Scholar] [CrossRef]
Figure 1. Research papers on AI (left) and AI in the power system (right).
Figure 1. Research papers on AI (left) and AI in the power system (right).
Energies 16 01077 g001
Figure 2. Search methodology and key search terms.
Figure 2. Search methodology and key search terms.
Energies 16 01077 g002
Figure 3. AI techniques used in the reviewed literature.
Figure 3. AI techniques used in the reviewed literature.
Energies 16 01077 g003
Figure 4. The AI-aided new energy paradigm.
Figure 4. The AI-aided new energy paradigm.
Energies 16 01077 g004
Figure 5. AI-aided fault recovery.
Figure 5. AI-aided fault recovery.
Energies 16 01077 g005
Figure 6. AI-aided Energy Management System.
Figure 6. AI-aided Energy Management System.
Energies 16 01077 g006
Figure 7. Digitally-enabled transaction network as a basic layer of the smart grid platform.
Figure 7. Digitally-enabled transaction network as a basic layer of the smart grid platform.
Energies 16 01077 g007
Figure 8. (a) Main areas of application of AI companies in the power sector. (b) Main focus areas of AI companies in the power sector.
Figure 8. (a) Main areas of application of AI companies in the power sector. (b) Main focus areas of AI companies in the power sector.
Energies 16 01077 g008
Figure 9. AI-based companies in the power sector across the Globe.
Figure 9. AI-based companies in the power sector across the Globe.
Energies 16 01077 g009
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Franki, V.; Majnarić, D.; Višković, A. A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector. Energies 2023, 16, 1077.

AMA Style

Franki V, Majnarić D, Višković A. A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector. Energies. 2023; 16(3):1077.

Chicago/Turabian Style

Franki, Vladimir, Darin Majnarić, and Alfredo Višković. 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector" Energies 16, no. 3: 1077.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop