Machine learning for electric power prediction: a systematic literature review
Article Sidebar
Main Article Content
This study presents a Systematic Literature Review (SLR) on artificial intelligence (AI) techniques applied to electric power prediction. The specialized databases employed in this review are Scopus, IEEE, ACM, and Google Scholar. This analysis provided a perspective on the artificial intelligence techniques utilized in this field, facilitating the identification of current and emerging trends. This offers a clear understanding of upcoming opportunities to enhance accuracy in electric power prediction and, consequently, decision-making.
A notable finding of this review was the predominant usage of Artificial Neural Networks (ANN) as the most prevalent technique within the field of Machine Learning applied to electric power prediction. This preference is justified by the inherent ability of ANN to identify complex patterns and relationships in data, making them a valuable tool for electric power prediction. Additionally, the importance of various fundamental factors in electric power prediction is highlighted, such as the significance of collecting relevant and representative data, encompassing both historical and contextual information. Data preprocessing, which involves cleaning and transforming collected data to properly prepare them for analysis and modeling, and data splitting, crucial for avoiding biases and accurately evaluating the predictive capability of the model, are also emphasized.
Zuitao Ma. Neural Networks Used for Time Series Prediction of Power Consumption. University of Oslo; 2019.
Barón A, Zapata C. Estrategia Metodológica para la Elaboración de Síntesis Conceptuales en Ingeniería de Software: una Aplicación al Caso del Constructo Teórico de Práctica. 4th Int Conf Softw Eng Res Innov CONISOFT. 2016;
Charters BK and S. Guidelines for performing systematic literature reviews in software engineering. Tech report, Ver 23 EBSE Tech Report EBSE. 2007;1:1–54.
Revelo-Sánchez O, Collazos-Ordóñez CA, Jiménez-Toledo JA. El trabajo colaborativo como estrategia didáctica para la enseñanza/aprendizaje de la programación: una revisión sistemática de literatura. TecnoLógicas. 2018;21(41). DOI: https://doi.org/10.22430/22565337.731
Gómez Sarduy JR, Monteagudo Yanes JP, Granado Rodríguez ME, Quiñones Ferreira JL, Torres YM. Determining cement ball mill dosage by artificial intelligence tools aimed at reducing energy consumption and environmental impact. Ing e Investig [Internet]. 2013 Sep 1;33(3):49–54. Available from: https://revistas.unal.edu.co/index.php/ingeinv/article/view/41043 DOI: https://doi.org/10.15446/ing.investig.v33n3.41043
Chaouachi A, Kamel RM, Andoulsi R, Nagasaka K. Multiobjective Intelligent Energy Management for a Microgrid. IEEE Trans Ind Electron [Internet]. 2013 Apr;60(4):1688–99. Available from: http://ieeexplore.ieee.org/document/6157610/ DOI: https://doi.org/10.1109/TIE.2012.2188873
Al-Daraiseh A, El-Qawasmeh E, Shah N. Multi-agent system for energy consumption optimisation in higher education institutions. J Comput Syst Sci [Internet]. 2015 Sep;81(6):958–65. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0022000014001743 DOI: https://doi.org/10.1016/j.jcss.2014.12.010
Yuce B, Rezgui Y, Mourshed M. ANN–GA smart appliance scheduling for optimised energy management in the domestic sector. Energy Build [Internet]. 2016 Jan;111:311–25. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0378778815303820 DOI: https://doi.org/10.1016/j.enbuild.2015.11.017
Saleh AI, Rabie AH, Abo-Al-Ez KM. A data mining based load forecasting strategy for smart electrical grids. Adv Eng Informatics [Internet]. 2016 Aug;30(3):422–48. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1474034616301331 DOI: https://doi.org/10.1016/j.aei.2016.05.005
Olatomiwa Lanre J. Optimal planning and design of hybrid renewable energy system for rural healthcare facilities / Olatomiwa Lanre Joseph. 2016; DOI: https://doi.org/10.1016/j.egyr.2016.06.001
Bose BK. Artificial Intelligence Techniques in Smart Grid and Renewable Energy Systems—Some Example Applications. Proc IEEE [Internet]. 2017 Nov;105(11):2262–73. Available from: http://ieeexplore.ieee.org/document/8074546/ DOI: https://doi.org/10.1109/JPROC.2017.2756596
Mutombo NMA. Development of neuro-fuzzy strategies for prediction and management of hybrid PV-PEMFC-battery systems. University of KwaZulu-Natal; 2017.
Vantuch T, Prilepok M. An ensemble of multi-objective optimized fuzzy regression models for short-term electric load forecasting. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI) [Internet]. IEEE; 2017. p. 1–7. Available from: http://ieeexplore.ieee.org/document/8285348/ DOI: https://doi.org/10.1109/SSCI.2017.8285348
Chou JS, Tran DS. Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy [Internet]. 2018 Dec;165:709–26. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0360544218319145 DOI: https://doi.org/10.1016/j.energy.2018.09.144
Hussein H. An Optimal Design Methodology of Adaptive Neuro-Fuzzy Inference System for Energy Load Forecasting - Hail city case study (Saudi Arabia). In: Proceedings of the Fourth International Conference on Engineering & MIS 2018 [Internet]. New York, NY, USA: ACM; 2018. p. 1–7. Available from: https://dl.acm.org/doi/10.1145/3234698.3234765 DOI: https://doi.org/10.1145/3234698.3234765
Borja Pozo L. Double Smart Energy Harvesting System for self-powered Industrial IoT. Universidad del País Vasco; 2018.
Alhebshi F, Alnabilsi H, Bensenouci A, Brahimi T. Using artificial intelligence techniques for solar irradiation forecasting: The case of Saudi Arabia. In: Proceedings of the International Conference on Industrial Engineering and Operations Management. 2019. p. 926–7.
Fathi S, Srinivasan R. Climate Change Impacts on Campus Buildings Energy Use. In: Proceedings of the 1st ACM International Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization [Internet]. New York, NY, USA: ACM; 2019. p. 112–9. Available from: https://dl.acm.org/doi/10.1145/3363459.3363540 DOI: https://doi.org/10.1145/3363459.3363540
Grigoraș G, Neagu BC. An Advanced Decision Support Platform in Energy Management to Increase Energy Efficiency for Small and Medium Enterprises. Appl Sci [Internet]. 2020 May 19;10(10):3505. Available from: https://www.mdpi.com/2076-3417/10/10/3505 DOI: https://doi.org/10.3390/app10103505
Waheed W, Xu Q. Optimal Short Term Power Load Forecasting Algorithm by Using Improved Artificial Intelligence Technique. In: 2020 2nd International Conference on Computer and Information Sciences (ICCIS) [Internet]. IEEE; 2020. p. 1–4. Available from: https://ieeexplore.ieee.org/document/9257675/ DOI: https://doi.org/10.1109/ICCIS49240.2020.9257675
Timur O, Zor K, Çelik Ö, Teke A, İbrikçi T. Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital. J Sustain Dev Energy, Water Environ Syst [Internet]. 2020 Sep;8(3):520–36. Available from: http://www.sdewes.org/jsdewes/pid7.0306 DOI: https://doi.org/10.13044/j.sdewes.d7.0306
Tran DH, Luong DL, Chou JS. Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings. Energy [Internet]. 2020 Jan;191:116552. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0360544219322479 DOI: https://doi.org/10.1016/j.energy.2019.116552
Cáceres L, Merino JI, Díaz-Díaz N. A Computational Intelligence Approach to Predict Energy Demand Using Random Forest in a Cloudera Cluster. Appl Sci [Internet]. 2021 Sep 17;11(18):8635. Available from: https://www.mdpi.com/2076-3417/11/18/8635 DOI: https://doi.org/10.3390/app11188635
Rocha HRO, Honorato IH, Fiorotti R, Celeste WC, Silvestre LJ, Silva JAL. An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes. Appl Energy [Internet]. 2021 Jan;282:116145. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0306261920315555 DOI: https://doi.org/10.1016/j.apenergy.2020.116145
Rizwan M, Alharbi YR. Artificial Intelligence Based Approach for Short Term Load Forecasting for Selected Feeders at Madina, Saudi Arabia. Int J Electr Electron Eng Telecommun [Internet]. 2021;10(5):300–6. Available from: http://www.ijeetc.com/index.php?m=content&c=index&a=show&catid=213&id=1534 DOI: https://doi.org/10.18178/ijeetc.10.5.300-306
Mui KW, Wong LT, Satheesan MK, Balachandran A. A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong. Energies [Internet]. 2021 Aug 9;14(16):4850. Available from: https://www.mdpi.com/1996-1073/14/16/4850 DOI: https://doi.org/10.3390/en14164850
Khan A, Rizwan M. ANN and ANFIS Based Approach for Very Short Term Load Forecasting: A Step Towards Smart Energy Management System. In: 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) [Internet]. IEEE; 2021. p. 464–8. Available from: https://ieeexplore.ieee.org/document/9566146/ DOI: https://doi.org/10.1109/SPIN52536.2021.9566146
Hajjaji I, Alami H El, El-Fenni MR, Dahmouni H. Evaluation of Artificial Intelligence Algorithms for Predicting Power Consumption in University Campus Microgrid. In: 2021 International Wireless Communications and Mobile Computing (IWCMC) [Internet]. IEEE; 2021. p. 2121–6. Available from: https://ieeexplore.ieee.org/document/9498891/ DOI: https://doi.org/10.1109/IWCMC51323.2021.9498891
Chen Y, Phelan P. Predicting peak energy demand for an office building using artificial intelligence (ai) approaches. In: American Society of Mechanical Engineers, Power Division (Publication) POWER. 2021. DOI: https://doi.org/10.1115/POWER2021-64492
Raju L, K P V, S SAA, V V, V B. Building Energy Management and Conservation using Internet of Things. In: 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) [Internet]. IEEE; 2022. p. 970–4. Available from: https://ieeexplore.ieee.org/document/9760907/ DOI: https://doi.org/10.1109/ICSCDS53736.2022.9760907
Alsaidan I, Rizwan M, Alaraj M. Solar energy forecasting using intelligent techniques: A step towards sustainable power generating system. Malik H, Chaudhary G, Srivastava S, editors. J Intell Fuzzy Syst [Internet]. 2022 Jan 25;42(2):885–96. Available from: https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JIFS-189757 DOI: https://doi.org/10.3233/JIFS-189757
Sasaki Y, Ueoka M, Uesugi Y, Yorino N, Zoka Y, Bedawy A, et al. A Robust Economic Load Dispatch in Community Microgrid Considering Incentive-based Demand Response. IFAC-PapersOnLine [Internet]. 2022;55(9):389–94. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2405896322004530 DOI: https://doi.org/10.1016/j.ifacol.2022.07.068
Grimaccia F, Niccolai A, Mussetta M, D’Alessandro G. ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants. Appl Sci [Internet]. 2023 Jan 20;13(3):1368. Available from: https://www.mdpi.com/2076-3417/13/3/1368 DOI: https://doi.org/10.3390/app13031368
Manchalwar AD, Patne NR, Vardhan BVS, Khedkar M. Peer-to-peer energy trading in a distribution network considering the impact of short-term load forecasting. Electr Eng [Internet]. 2023 Aug 19;105(4):2069–81. Available from: https://link.springer.com/10.1007/s00202-023-01793-8 DOI: https://doi.org/10.1007/s00202-023-01793-8
Selvaraj R, Kuthadi VM, Baskar S. Smart building energy management and monitoring system based on artificial intelligence in smart city. Sustain Energy Technol Assessments. 2023;56. DOI: https://doi.org/10.1016/j.seta.2023.103090
Millo F, Rolando L, Tresca L, Pulvirenti L. Development of a neural network-based energy management system for a plug-in hybrid electric vehicle. Transp Eng [Internet]. 2023 Mar;11:100156. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2666691X22000549 DOI: https://doi.org/10.1016/j.treng.2022.100156
Kim G, Lee G, An S, Lee J. Forecasting future electric power consumption in Busan New Port using a deep learning model. Asian J Shipp Logist [Internet]. 2023 Jun;39(2):78–93. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2092521223000184 DOI: https://doi.org/10.1016/j.ajsl.2023.04.001
El Bakali S, Ouadi H, Gheouany S. Solar Radiation Forecasting Using Artificial Intelligence Techniques for Energy Management System. In: Lecture Notes in Networks and Systems [Internet]. 2023. p. 408–21. Available from: https://link.springer.com/10.1007/978-3-031-35245-4_38 DOI: https://doi.org/10.1007/978-3-031-35245-4_38
Li F, Wan Z, Koch T, Zan G, Li M, Zheng Z, et al. Improving the accuracy of multi-step prediction of building energy consumption based on EEMD-PSO-Informer and long-time series. Comput Electr Eng. 2023;110. DOI: https://doi.org/10.1016/j.compeleceng.2023.108845
Martinez SB. Energy management systems for smart homes and local energy communities based on optimization and artificial intelligence techniques. Universidad Politécnica de Cataluña; 2023.
Mohamed K, Elnaz Y, Elaheh Y, Mehdi Zareian J. The Role of Mechanical Energy Storage Systems Based on Artificial IntelligenceTechniques in Future Sustainable Energy Systems. Int J Electr Eng Sustain [Internet]. 2023;1:22. Available from: https://ijees.org/index.php/ijees/index
El Abbadi N, El Youbi MS. ENHANCING WIND ENERGY FORECASTING THROUGH THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES: A COMPREHENSIVE STUDY. Int J Tech Phys Probl Eng. 2023;15(3).
Abdul Baseer M, Almunif A, Alsaduni I, Tazeen N. Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques. Energies. 2023;16(18). DOI: https://doi.org/10.3390/en16186414
Sadeghian Broujeny R, Ben Ayed S, Matalah M. Energy Consumption Forecasting in a University Office by Artificial Intelligence Techniques: An Analysis of the Exogenous Data Effect on the Modeling. Energies. 2023;16(10). DOI: https://doi.org/10.3390/en16104065
Abdulwahid AH. Artificial Intelligence-based Control Techniques for HVDC Systems. Vol. 7, Emerging Science Journal. 2023. DOI: https://doi.org/10.28991/ESJ-2023-07-02-024
Harrou F, Sun Y, Taghezouit B, Dairi A. Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting. Vol. 16, Energies. 2023. DOI: https://doi.org/10.3390/en16186731
Zhao M. Modernized Power System Optimal Operation & Safety Protection through Mathematical and Artificial Intelligence Techniques. University of Pittsburgh; 2023.
Fiorotti R, Rocha HRO, Coutinho CR, Rueda-Medina AC, Nardoto AF, Fardin JF. A novel strategy for simultaneous active/reactive power design and management using artificial intelligence techniques. Energy Convers Manag. 2023;294. DOI: https://doi.org/10.1016/j.enconman.2023.117565
Versaci M, La Foresta F. Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System. Energies. 2024;17(2). DOI: https://doi.org/10.3390/en17020402
Zahraoui Y, Korotko T, Mekhilef S, Rosin A. ANN-LSTM Based Tool for Photovoltaic Power Forecasting. In: 4th International Conference on Smart Grid and Renewable Energy, SGRE 2024 - Proceedings. 2024. DOI: https://doi.org/10.1109/SGRE59715.2024.10428969
Accepted 2024-06-17
Published 2024-07-11
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors grant the journal and Universidad del Valle the economic rights over accepted manuscripts, but may make any reuse they deem appropriate for professional, educational, academic or scientific reasons, in accordance with the terms of the license granted by the journal to all its articles.
Articles will be published under the Creative Commons 4.0 BY-NC-SA licence (Attribution-NonCommercial-ShareAlike).