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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 7887

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Special Issue Editor

Dr. Sara Carcangiu

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Guest Editor

Electrical and Electronic Engineering Department, University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy
Interests: smart distribution network planning and operation; distributed generation; demand response; energy flexibility; power systems; modeling and simulation; inverse problems; methods of artificial intelligence

Special Issue Information

Dear Colleagues,

It is challenging to imagine today a modern world without artificial intelligence. Currently, artificial intelligence surrounds us at every step. Its application is increasing not only in traditional application areas, but also in newer areas, including energy management systems, renewable energy conversion systems, electric aircrafts, aviation, electric vehicles, unmanned propulsion systems, robotics, etc.

An energy system can be a combination of mechanical, chemical, and electrical features, and it can cover various dimensions of energy types that include renewables and other alternative energy systems as well.

As the demand for energy continues to increase, smart energy systems are becoming more prevalent in addressing the challenges associated with energy generation, distribution, and consumption. Artificial intelligence and machine learning have been identified as promising approaches to address these challenges as they improve the efficiency, reliability, and sustainability of smart energy systems.

The main goal of this Special Issue is to bring together the latest research and developments in the areas of artificial intelligence and machine learning for smart energy systems.

Original research articles, review papers, and case studies that demonstrate innovative applications of artificial intelligence and machine learning in energy systems are welcome.

Topics of interest for publication include, but are not limited to:

Energy management system algorithms;
Machine learning for energy forecasting;
Load forecasting;
Energy consumption/production analysis, modelling and prediction by means of neural networks;
Data processing in energy management systems;
Neural network models and relations in energy management systems;
Novel applications in energy management systems;
Advanced modelling approaches of energy systems;
User-oriented energy management systems designs;
IoT—Internet of Things (industrial Internet of Things);
Renewable energy sources;
Artificial intelligence in demand response;
Intelligent control and optimization of energy systems;
Big data analytics for smart grids;
Reinforcement learning for energy management;
Deep learning for energy system modelling and simulation;
Human–machine interactions and decision making in smart energy systems.
Energy systems’ flexibility, efficiency, and power quality;
Machine learning and deep learning models for mitigation of wind power fluctuation and methods for power generation;
Prediction of levelized cost of electricity;
Classifications using deep learning or advanced machine learning for power quality disturbances;
Electricity market price prediction using advanced machine learning;
Case studies can include the following topics: electric vehicles, energy investments, network planning, etc.
Case study on combined applications of machine learning, IoT, and big data for energy efficiency.

Dr. Sara Carcangiu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

optimization
prediction
performance evaluation
IoT
classification
deep learning
machine learning
power systems

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Published Papers (5 papers)

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Research

Jump to: Review

15 pages, 2795 KiB

Open AccessArticle

Estimating Snow Coverage Percentage on Solar Panels Using Drone Imagery and Machine Learning for Enhanced Energy Efficiency

by Ashraf Saleem, Ali Awad, Amna Mazen, Zoe Mazurkiewicz and Ana Dyreson

Energies 2025, 18(7), 1729; https://doi.org/10.3390/en18071729 - 31 Mar 2025

Viewed by 372

Abstract

Snow accumulation on solar panels presents a significant challenge to energy generation in snowy regions, reducing the efficiency of solar photovoltaic (PV) systems and impacting economic viability. While prior studies have explored snow detection using fixed-camera setups, these methods suffer from scalability limitations, stationary viewpoints, and the need for reference images. This study introduces an automated deep-learning framework that leverages drone-captured imagery to detect and quantify snow coverage on solar panels, aiming to enhance power forecasting and optimize snow removal strategies in winter conditions. We developed and evaluated two approaches using YOLO-based models: Approach 1, a high-precision method utilizing a two-class detection model, and Approach 2, a real-time single-class detection model optimized for fast inference. While Approach 1 demonstrated superior accuracy, achieving an overall precision of 89% and recall of 82%, it is computationally expensive, making it more suitable for strategic decision making. Approach 2, with a precision of 93% and a recall of 75%, provides a lightweight and efficient alternative for real-time monitoring but is sensitive to lighting variations. The proposed framework calculates snow coverage percentages (SCP) to support snow removal planning, minimize downtime, and optimize power generation. Compared to fixed-camera-based snow detection models, our approach leverages drone imagery to improve detection precision while offering greater scalability to be adopted for large solar farms. Qualitative and quantitative analysis of both approaches is presented in this paper, highlighting their strengths and weaknesses in different environmental conditions. Full article

(This article belongs to the Special Issue Application of Machine Learning Tools for Energy System)

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41 pages, 6828 KiB

Open AccessArticle

Energy Burden in the United States: An Analysis Using Decision Trees

by Jungwoo Chun, Dania Ortiz, Brooke Jin, Nikita Kulkarni, Stephen Hart and Janelle Knox-Hayes

Energies 2025, 18(3), 646; https://doi.org/10.3390/en18030646 - 30 Jan 2025

Viewed by 729

Abstract

The concept of energy burden (EB) continues to gain prominence in energy and associated policy research as energy prices rise and electricity and heating options diversify. This research offers a deeper understanding of EB dynamics and how EB can be addressed more effectively by discerning the interplay between regional environmental, social, and economic factors. Using decision trees (DTs), a powerful machine learning technique, we explore the multifaceted dynamics that shape EB across the United States (U.S.) by examining how factors like housing quality, demographic variations, access to energy sources, and regional economic conditions interact, creating distinct EB profiles across communities. Following a comprehensive review of existing literature and DT analysis, we map the results to identify the most significant factors influencing EB. We find that no single variable has a determinant effect on EB levels. While there is no uniform regional pattern, regions with higher population density exhibit a stronger correlation between EB and socioeconomic and other demographic factors such as educational attainment levels and racial segregation. Our findings underscore the significance of regional ecologies in shaping EB, revealing how localized environmental and economic contexts amplify or mitigate systemic inequities. Specifically, our analysis reveals significant regional disparities, highlighting the need for localized policies and interventions. We find that a one-size-fits-all approach is insufficient and that targeted, place-based strategies are necessary to address the specific needs of different communities. Policy interventions should prioritize energy democracy, address systemic inequities, and ensure universal energy access through participatory planning, financial assistance, and targeted initiatives such as housing rehabilitation, energy efficiency improvements, and incentives for underrepresented communities. Full article

(This article belongs to the Special Issue Application of Machine Learning Tools for Energy System)

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20 pages, 6342 KiB

Open AccessArticle

Low-Voltage Renewable Energy Communities’ Impact on the Distribution Networks

by Susanna Mocci, Simona Ruggeri and Fabrizio Pilo

Energies 2025, 18(1), 126; https://doi.org/10.3390/en18010126 - 31 Dec 2024

Cited by 2 | Viewed by 978

Abstract

Renewable energy communities (RECs) are widely regarded as a transformative opportunity to enhance the management of electricity distribution networks, benefiting the system as a whole and its participants through local energy production, increased self-consumption, and empowering citizens. However, their proliferation introduces significant challenges for distribution system management, particularly at the low-voltage (LV) level, where participants are primarily located. Despite its critical role, the LV network is often overlooked in favor of studies focusing on the system-level impacts. This paper addresses this gap by evaluating the impact of RECs on LV networks and the broader distribution system. The study analyzes various LV networks representative of the Italian context, encompassing both rural and urban areas. By leveraging the software tool OpenDSS and Monte Carlo simulations over an entire year, the analysis captures the inherent variability of load demand and photovoltaic generation, as well as the resulting network imbalances under diverse policy scenarios. The findings reveal that the increasing level of self-consumption could significantly challenge distribution network operation, limiting also the sourcing of flexibility. These results underscore the necessity for advanced management strategies and targeted investments in grid flexibility to ensure the reliability and efficiency of distribution networks integrating RECs. Full article

(This article belongs to the Special Issue Application of Machine Learning Tools for Energy System)

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21 pages, 3095 KiB

Open AccessFeature PaperArticle

Multi-Agent Reinforcement Learning for Smart Community Energy Management

by Patrick Wilk, Ning Wang and Jie Li

Energies 2024, 17(20), 5211; https://doi.org/10.3390/en17205211 - 20 Oct 2024

Cited by 1 | Viewed by 2076

Abstract

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management. Full article

(This article belongs to the Special Issue Application of Machine Learning Tools for Energy System)

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Review

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37 pages, 699 KiB

Open AccessFeature PaperReview

The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market

by Vasileios Laitsos, Georgios Vontzos, Paschalis Paraschoudis, Eleftherios Tsampasis, Dimitrios Bargiotas and Lefteri H. Tsoukalas

Energies 2024, 17(22), 5797; https://doi.org/10.3390/en17225797 - 20 Nov 2024

Cited by 3 | Viewed by 2886

Abstract

In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results. Full article

(This article belongs to the Special Issue Application of Machine Learning Tools for Energy System)

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