Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

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1. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
2. Li, Hongcheng & Yang, Dan & Cao, Huajun & Ge, Weiwei & Chen, Erheng & Wen, Xuanhao & Li, Chongbo, 2022. "Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system," Energy, Elsevier, vol. 239(PC).
3. Farzad Dadras Javan & Italo Aldo Campodonico Avendano & Behzad Najafi & Amin Moazami & Fabio Rinaldi, 2023. "Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses," Energies, MDPI, vol. 16(14), pages 1-15, July.
4. Chongchong Xu & Zhicheng Liao & Chaojie Li & Xiaojun Zhou & Renyou Xie, 2022. "Review on Interpretable Machine Learning in Smart Grid," Energies, MDPI, vol. 15(12), pages 1-31, June.
5. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
6. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
7. Zeyue Sun & Mohsen Eskandari & Chaoran Zheng & Ming Li, 2022. "Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-20, December.
8. Bhuiyan, Erphan A. & Hossain, Md. Zahid & Muyeen, S.M. & Fahim, Shahriar Rahman & Sarker, Subrata K. & Das, Sajal K., 2021. "Towards next generation virtual power plant: Technology review and frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
9. Musawenkosi Lethumcebo Thanduxolo Zulu & Rudiren Pillay Carpanen & Remy Tiako, 2023. "A Comprehensive Review: Study of Artificial Intelligence Optimization Technique Applications in a Hybrid Microgrid at Times of Fault Outbreaks," Energies, MDPI, vol. 16(4), pages 1-32, February.
10. Sijia Li & Arman Oshnoei & Frede Blaabjerg & Amjad Anvari-Moghaddam, 2023. "Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
11. Yilmaz, Selin & Xu, Xiaojing & Cabrera, Daniel & Chanez, Cédric & Cuony, Peter & Patel, Martin K., 2020. "Analysis of demand-side response preferences regarding electricity tariffs and direct load control: Key findings from a Swiss survey," Energy, Elsevier, vol. 212(C).
12. Santiago Bañales & Raquel Dormido & Natividad Duro, 2021. "Smart Meters Time Series Clustering for Demand Response Applications in the Context of High Penetration of Renewable Energy Resources," Energies, MDPI, vol. 14(12), pages 1-22, June.
13. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
14. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Demand side management in microgrid: A critical review of key issues and recent trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
15. Adrian Tantau & András Puskás-Tompos & Laurentiu Fratila & Costel Stanciu, 2021. "Acceptance of Demand Response and Aggregators as a Solution to Optimize the Relation between Energy Producers and Consumers in order to Increase the Amount of Renewable Energy in the Grid," Energies, MDPI, vol. 14(12), pages 1-19, June.
16. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
17. Nada Zine Laabidine & Badre Bossoufi & Ismail El Kafazi & Chakib El Bekkali & Najib El Ouanjli, 2023. "Robust Adaptive Super Twisting Algorithm Sliding Mode Control of a Wind System Based on the PMSG Generator," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
18. Lu, Hsi-Peng & Cheng, Hsiang-Ling & Tzou, Jen-Chuen & Chen, Chiao-Shan, 2023. "Technology roadmap of AI applications in the retail industry," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
19. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
20. Rafael E. Carrillo & Antonis Peppas & Yves Stauffer & Chrysa Politi & Tomasz Gorecki & Pierre-Jean Alet, 2022. "A Multilevel Control Approach to Exploit Local Flexibility in Districts Evaluated under Real Conditions," Energies, MDPI, vol. 15(16), pages 1-17, August.
21. Ibrahim, Charles & Mougharbel, Imad & Kanaan, Hadi Y. & Daher, Nivine Abou & Georges, Semaan & Saad, Maarouf, 2022. "A review on the deployment of demand response programs with multiple aspects coexistence over smart grid platform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
22. Senfeng Cen & Jae Hung Yoo & Chang Gyoon Lim, 2022. "Electricity Pattern Analysis by Clustering Domestic Load Profiles Using Discrete Wavelet Transform," Energies, MDPI, vol. 15(4), pages 1-18, February.
23. Kirli, Desen & Couraud, Benoit & Robu, Valentin & Salgado-Bravo, Marcelo & Norbu, Sonam & Andoni, Merlinda & Antonopoulos, Ioannis & Negrete-Pincetic, Matias & Flynn, David & Kiprakis, Aristides, 2022. "Smart contracts in energy systems: A systematic review of fundamental approaches and implementations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
24. Philippe de Bekker & Sho Cremers & Sonam Norbu & David Flynn & Valentin Robu, 2023. "Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm," Energies, MDPI, vol. 16(5), pages 1-26, March.
25. Noman, Abdullah Al & Tasneem, Zinat & Sahed, Md. Fahad & Muyeen, S.M. & Das, Sajal K. & Alam, Firoz, 2022. "Towards next generation Savonius wind turbine: Artificial intelligence in blade design trends and framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
26. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
27. Inês F. G. Reis & Ivo Gonçalves & Marta A. R. Lopes & Carlos Henggeler Antunes, 2021. "Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach," Energies, MDPI, vol. 14(4), pages 1-32, February.
28. Ana Cabrera-Tobar & Alessandro Massi Pavan & Giovanni Petrone & Giovanni Spagnuolo, 2022. "A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids," Energies, MDPI, vol. 15(23), pages 1-38, December.
29. Timothé Gronier & William Maréchal & Stéphane Gibout & Christophe Geissler, 2022. "Relevance of Optimized Low-Scale Green H 2 Systems in a French Context: Two Case Studies," Energies, MDPI, vol. 15(10), pages 1-21, May.
30. He, Xin & Wang, Ning & Zhou, Qiaoqiao & Huang, Jun & Ramakrishna, Seeram & Li, Fanghua, 2024. "Smart aviation biofuel energy system coupling with machine learning technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
31. Kah Phooi Seng & Li Minn Ang & Ericmoore Ngharamike, 2022. "Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks," International Journal of Distributed Sensor Networks, , vol. 18(3), pages 15501477211, March.
32. Zahra Jahangiri & Mackenzie Judson & Kwang Moo Yi & Madeleine McPherson, 2023. "A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System," Energies, MDPI, vol. 16(3), pages 1-21, January.
33. Zhang, Dongdong & Li, Chunjiao & Goh, Hui Hwang & Ahmad, Tanveer & Zhu, Hongyu & Liu, Hui & Wu, Thomas, 2022. "A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems," Renewable Energy, Elsevier, vol. 189(C), pages 1383-1406.
34. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
35. Suchismita Satapathy, 2023. "A Study on Occupational Health and Safety Practices on Construction Site Workers for Finding Discomfort Level," International Journal of Social Ecology and Sustainable Development (IJSESD), IGI Global, vol. 14(1), pages 1-15, January.
36. Tsaousoglou, Georgios & Giraldo, Juan S. & Paterakis, Nikolaos G., 2022. "Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
37. Panagiotis Michailidis & Paschalis Pelitaris & Christos Korkas & Iakovos Michailidis & Simone Baldi & Elias Kosmatopoulos, 2021. "Enabling Optimal Energy Management with Minimal IoT Requirements: A Legacy A/C Case Study," Energies, MDPI, vol. 14(23), pages 1-25, November.
38. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
39. Sebastian C. Ibañez & Christopher P. Monterola, 2023. "A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers," Agriculture, MDPI, vol. 13(9), pages 1-27, September.
40. Prina, Matteo Giacomo & Groppi, Daniele & Nastasi, Benedetto & Garcia, Davide Astiaso, 2021. "Bottom-up energy system models applied to sustainable islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
41. Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
42. Han, Gwangwoo & Joo, Hong-Jin & Lim, Hee-Won & An, Young-Sub & Lee, Wang-Je & Lee, Kyoung-Ho, 2023. "Data-driven heat pump operation strategy using rainbow deep reinforcement learning for significant reduction of electricity cost," Energy, Elsevier, vol. 270(C).
43. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
44. Lin, Sheng-Wei & Lu, Wen-Min, 2024. "Using inverse DEA and machine learning algorithms to evaluate and predict suppliers’ performance in the apple supply chain," International Journal of Production Economics, Elsevier, vol. 271(C).
45. Isaías Gomes & Rui Melicio & Victor M. F. Mendes, 2021. "Assessing the Value of Demand Response in Microgrids," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
46. Mitra, Somalee & Chakraborty, Basab & Mitra, Pabitra, 2024. "Smart meter data analytics applications for secure, reliable and robust grid system: Survey and future directions," Energy, Elsevier, vol. 289(C).
47. Fuster-Palop, Enrique & Prades-Gil, Carlos & Masip, X. & Viana-Fons, Joan D. & Payá, Jorge, 2021. "Innovative regression-based methodology to assess the techno-economic performance of photovoltaic installations in urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
48. Patrick Sunday Onen & Geev Mokryani & Rana H. A. Zubo, 2022. "Planning of Multi-Vector Energy Systems with High Penetration of Renewable Energy Source: A Comprehensive Review," Energies, MDPI, vol. 15(15), pages 1-25, August.
49. Tao Lv & Yuehong Lu & Yijie Zhou & Xuemei Liu & Changlong Wang & Yang Zhang & Zhijia Huang & Yanhong Sun, 2022. "Optimal Control of Energy Systems in Net-Zero Energy Buildings Considering Dynamic Costs: A Case Study of Zero Carbon Building in Hong Kong," Sustainability, MDPI, vol. 14(6), pages 1-25, March.
50. Sandra Giraldo & David la Rotta & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortúa, 2021. "Digital Transformation of Energy Companies: A Colombian Case Study," Energies, MDPI, vol. 14(9), pages 1-14, April.
51. Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
52. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
53. Davide Coraci & Silvio Brandi & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings," Energies, MDPI, vol. 14(4), pages 1-26, February.
54. Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility," Applied Energy, Elsevier, vol. 314(C).
55. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
56. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
57. Izzuddin Fathin Azhar & Lesnanto Multa Putranto & Roni Irnawan, 2022. "Development of PMU-Based Transient Stability Detection Methods Using CNN-LSTM Considering Time Series Data Measurement," Energies, MDPI, vol. 15(21), pages 1-20, November.
58. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
59. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
60. Białek, Jakub & Bujalski, Wojciech & Wojdan, Konrad & Guzek, Michał & Kurek, Teresa, 2022. "Dataset level explanation of heat demand forecasting ANN with SHAP," Energy, Elsevier, vol. 261(PA).
61. Raziq Yaqub & Mohamed Ali & Hassan Ali, 2021. "DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter," Energies, MDPI, vol. 14(19), pages 1-17, September.
62. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
63. Daiva Stanelyte & Neringa Radziukyniene & Virginijus Radziukynas, 2022. "Overview of Demand-Response Services: A Review," Energies, MDPI, vol. 15(5), pages 1-31, February.
64. Jiang, Nanhua & Zhang, Jiawei & Jiang, Weiran & Ren, Yao & Lin, Jing & Khoo, Edwin & Song, Ziyou, 2024. "Driving behavior-guided battery health monitoring for electric vehicles using extreme learning machine," Applied Energy, Elsevier, vol. 364(C).
65. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
66. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
67. Liang, Xinbin & Chen, Siliang & Zhu, Xu & Jin, Xinqiao & Du, Zhimin, 2023. "Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions," Applied Energy, Elsevier, vol. 344(C).
68. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).
69. Förster, Robert & Harding, Sebastian & Buhl, Hans Ulrich, 2024. "Unleashing the economic and ecological potential of energy flexibility: Attractiveness of real-time electricity tariffs in energy crises," Energy Policy, Elsevier, vol. 185(C).
70. Couraud, Benoit & Andoni, Merlinda & Robu, Valentin & Norbu, Sonam & Chen, Si & Flynn, David, 2023. "Responsive FLEXibility: A smart local energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).