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Artificial Intelligence in the Energy Industry

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: closed (28 October 2021) | Viewed by 22739

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Guest Editor
BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
Interests: applied artificial Intelligence; machine learning; intelligent control engineering; renewable energy engineering; system optimization; smart city; human–computer interaction
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Special Issue Information

Artificial intelligence is essential in every industrial environment. The energy industry is an area that presents exceptional opportunities for development with the use of AI. In essence, artificial intelligence provides a machine with the ability to learn and make decisions to solve problems or optimize results to meet a goal. There are many decisions to be made in the energy sector that need an early response and to handle a significant volume of data. Artificial intelligence can optimally perform these important decisions that require instantaneous collection and analysis of these large volumes of data while processing as fast and efficiently as possible.

Smart grids carry electricity, but also data. In the case of intermittent and volatile energies, such as solar and wind, it is more important than ever to effectively balance consumption and generation. One of the hopes for artificial intelligence applied to the energy sector is that it will help us with climate change, emission-reduction effects of technological progresses in industry, energy balances and environmental impact, etc. One of the most basic applications is that machine learning (or automatic learning, a key part of artificial intelligence) helps to make generation systems more efficient, improving the efficiency of design technologies and creating energy-efficient objects.

The future of mobility is electric, but that also poses new challenges. AI is being installed in the electric vehicle sector within cars themselves in order to manage it and communicate data that contribute to solving these challenges, but also outside the car to facilitate the effective management of reports, intelligent mobility solutions, etc.

Artificial intelligence is beginning to be used in the energy sector and is already proving essential by providing the industry and households with new information services in the control over energy infrastructure, optimizing generation, reducing consumption or fighting climate change, which are only some of the promises it holds in the near future.

Dr. Ana-Belén Gil-González
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

  • AI
  • Machine learning
  • Big Data
  • Deep learning
  • IoT
  • Renewable energy
  • Virtual power plants
  • Smart grids
  • Power grid
  • Energy system
  • Optimization techniques
  • Control methods
  • Energy storage system
  • Energy consumption
  • Smart forecasting

Published Papers (7 papers)

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Research

20 pages, 617 KiB  
Article
Towards a Blockchain-Based Peer-to-Peer Energy Marketplace
by Yeray Mezquita, Ana Belén Gil-González, Angel Martín del Rey, Javier Prieto and Juan Manuel Corchado
Energies 2022, 15(9), 3046; https://doi.org/10.3390/en15093046 - 21 Apr 2022
Cited by 14 | Viewed by 2246
Abstract
Blockchain technology is used as a distributed ledger to store and secure data and perform transactions between entities in smart grids. This paper proposes a platform based on blockchain technology and the multi-agent system paradigm to allow for the creation of an automated [...] Read more.
Blockchain technology is used as a distributed ledger to store and secure data and perform transactions between entities in smart grids. This paper proposes a platform based on blockchain technology and the multi-agent system paradigm to allow for the creation of an automated peer-to-peer electricity market in micro-grids. The use of a permissioned blockchain network has multiple benefits as it reduces transaction costs and enables micro-transactions. Moreover, an improvement in security is obtained, eliminating the single point of failure in the control and management of the platform along with creating the possibility to trace back the actions of the participants and a mechanism of identification. Furthermore, it provides the opportunity to create a decentralized and democratic energy market while complying with the current legislation and regulations on user privacy and data protection by incorporating Zero-Knowledge Proof protocols and ring signatures. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Energy Industry)
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18 pages, 6113 KiB  
Article
Application of Artificial Neural Networks for Virtual Energy Assessment
by Amir Mortazavigazar, Nourehan Wahba, Paul Newsham, Maharti Triharta, Pufan Zheng, Tracy Chen and Behzad Rismanchi
Energies 2021, 14(24), 8330; https://doi.org/10.3390/en14248330 - 10 Dec 2021
Viewed by 3099
Abstract
A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting [...] Read more.
A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as ‘operational energy’ in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Energy Industry)
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14 pages, 14367 KiB  
Article
Energy Management Model for HVAC Control Supported by Reinforcement Learning
by Pedro Macieira, Luis Gomes and Zita Vale
Energies 2021, 14(24), 8210; https://doi.org/10.3390/en14248210 - 7 Dec 2021
Cited by 8 | Viewed by 2168
Abstract
Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method [...] Read more.
Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method applied in this paper divides the addressed problem into three steps: (i) the continuous acquisition of data provided by an open-source building energy management systems, (ii) the proposed learning and predictive model able to predict if users will be working in a given location, and (iii) the proposed decision model to manage the HVAC units according to the prediction of users, current environmental context, and current energy prices. The results show that the proposed predictive model was able to achieve a 93.8% accuracy and that the proposed decision tree enabled the maintenance of users’ comfort. The results demonstrate that the proposed solution is able to run in real-time in a real office building, making it a possible solution for smart buildings. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Energy Industry)
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15 pages, 1843 KiB  
Article
Forecasting Brazilian Ethanol Spot Prices Using LSTM
by Gustavo Carvalho Santos, Flavio Barboza, Antônio Cláudio Paschoarelli Veiga and Mateus Ferreira Silva
Energies 2021, 14(23), 7987; https://doi.org/10.3390/en14237987 - 30 Nov 2021
Cited by 2 | Viewed by 3321
Abstract
Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent [...] Read more.
Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Energy Industry)
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30 pages, 2357 KiB  
Article
A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain
by Mahmoud Abdelkader Bashery Abbass and Mohamed Hamdy
Energies 2021, 14(17), 5410; https://doi.org/10.3390/en14175410 - 31 Aug 2021
Cited by 4 | Viewed by 2435
Abstract
One of the biggest problems in applying machine learning (ML) in the energy and buildings field is the lack of experience of ML users in implementing each ML algorithm in real-life applications the right way, because each algorithm has prerequisites to be used [...] Read more.
One of the biggest problems in applying machine learning (ML) in the energy and buildings field is the lack of experience of ML users in implementing each ML algorithm in real-life applications the right way, because each algorithm has prerequisites to be used and specific problems or applications to be implemented. Hence, this paper introduces a generic pipeline to the ML users in the specified field to guide them to select the best-fitting algorithm based on their particular applications and to help them to implement the selected algorithm correctly to achieve the best performance. The introduced pipeline is built on (1) reviewing the most popular trails to put ML pipelines for the energy and building, with a declaration for each trial drawbacks to avoid it in the proposed pipeline; (2) reviewing the most popular ML algorithms in the energy and buildings field and linking them with possible applications in the energy and buildings field in one layout; (3) a full description of the proposed pipeline by explaining the way of implementing it and its environmental impacts in improving energy management systems for different countries; and (4) implementing the pipeline on real data (CBECS) to prove its applicability. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Energy Industry)
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14 pages, 1964 KiB  
Article
An Artificial Intelligence Empowered Cyber Physical Ecosystem for Energy Efficiency and Occupation Health and Safety
by Petros Koutroumpinas, Yu Zhang, Steve Wallis and Elizabeth Chang
Energies 2021, 14(14), 4214; https://doi.org/10.3390/en14144214 - 12 Jul 2021
Cited by 2 | Viewed by 2339
Abstract
Reducing energy waste is one of the primary concerns facing Remote Industrial Plants (RIP) and, in particular, the accommodations and operational plants located in remote areas. With the COVID-19 pandemic continuing to attack the health of workforce, managing the balance between energy efficiency [...] Read more.
Reducing energy waste is one of the primary concerns facing Remote Industrial Plants (RIP) and, in particular, the accommodations and operational plants located in remote areas. With the COVID-19 pandemic continuing to attack the health of workforce, managing the balance between energy efficiency and Occupation Health and Safety (OHS) in the workplace becomes another great challenge for the RIP. Maintaining this balance is difficult mainly because a full awareness of the OHS will generally consume more energy while reducing the energy cost may lead to a less effective OHS, and the existing literature has not seen a system that is designed for the RIPs to conserve energy usage and improve workforce OHS simultaneously. To bridge this gap, in this paper, we propose an AI Empowered Cyber Physical Ecosystem (AECPE) solution for the RIPs, which integrates Cyber-Physical Systems (CPS), artificial intelligence, and mobile networks. The preliminary results of lab experiments and field tests proved that the AECPE was able to help industries reduce the corporate annual energy cost that is worth millions of dollars, optimise the environmental conditions, and improve OHS for all workers and stakeholders. The implementation of the AECPE can result in efficient energy usage, reduced wastage and emissions, environment-friendly operations, and improved social reputation of the industries. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Energy Industry)
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20 pages, 5620 KiB  
Article
Comparison of Heat Demand Prediction Using Wavelet Analysis and Neural Network for a District Heating Network
by Szabolcs Kováč, German Micha’čonok, Igor Halenár and Pavel Važan
Energies 2021, 14(6), 1545; https://doi.org/10.3390/en14061545 - 11 Mar 2021
Cited by 11 | Viewed by 2807
Abstract
Short-Term Load Prediction (STLP) is an important part of energy planning. STLP is based on the analysis of historical data such as outdoor temperature, heat load, heat consumer configuration, and the seasons. This research aims to forecast heat consumption during the winter heating [...] Read more.
Short-Term Load Prediction (STLP) is an important part of energy planning. STLP is based on the analysis of historical data such as outdoor temperature, heat load, heat consumer configuration, and the seasons. This research aims to forecast heat consumption during the winter heating season. By preprocessing and analyzing the data, we can determine the patterns in the data. The results of the data analysis make it possible to form learning algorithms for an artificial neural network (ANN). The biggest disadvantage of an ANN is the lack of precise guidelines for architectural design. Another disadvantage is the presence of false information in the analyzed training data. False information is the result of errors in measuring, collecting, and transferring data. Usually, trial error techniques are used to determine the number of hidden nodes. To compare prediction accuracy, several models have been proposed, including a conventional ANN and a wavelet ANN. In this research, the influence of different learning algorithms was also examined. The main differences were the training time and number of epochs. To improve the quality of the raw data and remove false information, the research uses the technology of normalizing raw data. The basis of normalization was the technology of the Z-score of the data and determination of the energy‒entropy ratio. The purpose of this research was to compare the accuracy of various data processing and neural network training algorithms suitable for use in data-driven (black box) modeling. For this research, we used a software application created in the MATLAB environment. The app uses wavelet transforms to compare different heat demand prediction methods. The use of several wavelet transforms for various wavelet functions in the research allowed us to determine the best algorithm and method for predicting heat production. The results of the research show the need to normalize the raw data using wavelet transforms. The sequence of steps involves following milestones: normalization of initial data, wavelet analysis employing quantitative criteria (energy, entropy, and energy‒entropy ratio), optimization of ANN training with information energy–entropy ratio, ANN training with different training algorithms, and evaluation of obtained outputs using statistical methods. The developed application can serve as a control tool for dispatchers during planning. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Energy Industry)
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