My bibliography
Save this item
Reinforcement learning for demand response: A review of algorithms and modeling techniques
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Seongwoo Lee & Joonho Seon & Byungsun Hwang & Soohyun Kim & Youngghyu Sun & Jinyoung Kim, 2024. "Recent Trends and Issues of Energy Management Systems Using Machine Learning," Energies, MDPI, vol. 17(3), pages 1-24, January.
- 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).
- Kim, Sunwoo & Choi, Yechan & Park, Joungho & Adams, Derrick & Heo, Seongmin & Lee, Jay H., 2024. "Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
- Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
- Nik, Vahid M. & Hosseini, Mohammad, 2023. "CIRLEM: a synergic integration of Collective Intelligence and Reinforcement learning in Energy Management for enhanced climate resilience and lightweight computation," Applied Energy, Elsevier, vol. 350(C).
- Pavlos S. Georgilakis, 2020. "Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Researc," Energies, MDPI, vol. 13(1), pages 1-37, January.
- Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
- Kou, Peng & Liang, Deliang & Wang, Chen & Wu, Zihao & Gao, Lin, 2020. "Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks," Applied Energy, Elsevier, vol. 264(C).
- Jingwen Yang & Ping Wang & Yongfeng Ju, 2024. "Variable Speed Limit Intelligent Decision-Making Control Strategy Based on Deep Reinforcement Learning under Emergencies," Sustainability, MDPI, vol. 16(3), pages 1-19, January.
- Chen, Zhiwen & Deng, Qiao & Ren, Hao & Zhao, Zhengrun & Peng, Tao & Yang, Chunhua & Gui, Weihua, 2022. "A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data," Applied Energy, Elsevier, vol. 310(C).
- Pinto, Giuseppe & Kathirgamanathan, Anjukan & Mangina, Eleni & Finn, Donal P. & Capozzoli, Alfonso, 2022. "Enhancing energy management in grid-interactive buildings: A comparison among cooperative and coordinated architectures," Applied Energy, Elsevier, vol. 310(C).
- Lin, Xinyou & Zeng, Songrong & Li, Xuefan, 2021. "Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network," Energy, Elsevier, vol. 223(C).
- Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
- Carol Vigurs & Chris Maidment & Michael Fell & David Shipworth, 2021. "Customer Privacy Concerns as a Barrier to Sharing Data about Energy Use in Smart Local Energy Systems: A Rapid Realist Review," Energies, MDPI, vol. 14(5), pages 1-33, February.
- Dorokhova, Marina & Martinson, Yann & Ballif, Christophe & Wyrsch, Nicolas, 2021. "Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation," Applied Energy, Elsevier, vol. 301(C).
- Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
- Abou-Foul, Mohamad & Ruiz-Alba, Jose L. & López-Tenorio, Pablo J., 2023. "The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective," Journal of Business Research, Elsevier, vol. 157(C).
- Jun Dong & A-Ru-Han Bao & Yao Liu & Xi-Hao Dou & Dong-Ran Liu & Gui-Yuan Xue, 2022. "Dynamic Differential Game Strategy of the Energy Big Data Ecosystem Considering Technological Innovation," Sustainability, MDPI, vol. 14(12), pages 1-24, June.
- Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
- 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.
- Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
- Kinga Stecuła & Radosław Wolniak & Wieslaw Wes Grebski, 2023. "AI-Driven Urban Energy Solutions—From Individuals to Society: A Review," Energies, MDPI, vol. 16(24), pages 1-34, December.
- Caputo, Cesare & Cardin, Michel-Alexandre & Ge, Pudong & Teng, Fei & Korre, Anna & Antonio del Rio Chanona, Ehecatl, 2023. "Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning," Applied Energy, Elsevier, vol. 335(C).
- Qiu, Dawei & Wang, Yi & Hua, Weiqi & Strbac, Goran, 2023. "Reinforcement learning for electric vehicle applications in power systems:A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
- 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).
- Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
- Sun, Fangyuan & Kong, Xiangyu & Wu, Jianzhong & Gao, Bixuan & Chen, Ke & Lu, Ning, 2022. "DSM pricing method based on A3C and LSTM under cloud-edge environment," Applied Energy, Elsevier, vol. 315(C).
- Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
- Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
- Charbonnier, Flora & Morstyn, Thomas & McCulloch, Malcolm D., 2022. "Coordination of resources at the edge of the electricity grid: Systematic review and taxonomy," Applied Energy, Elsevier, vol. 318(C).
- Christian Blad & Simon Bøgh & Carsten Kallesøe, 2021. "A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems," Energies, MDPI, vol. 14(22), pages 1-20, November.
- Wang, Yi & Qiu, Dawei & Strbac, Goran, 2022. "Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems," Applied Energy, Elsevier, vol. 310(C).
- 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.
- 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).
- Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
- Liudong Chen & Bolun Xu, 2024. "Prudent Price-Responsive Demands," Papers 2405.16356, arXiv.org.
- Nik, Vahid M. & Moazami, Amin, 2021. "Using collective intelligence to enhance demand flexibility and climate resilience in urban areas," Applied Energy, Elsevier, vol. 281(C).
- Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
- Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
- Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(C).
- Guo, Yuxiang & Qu, Shengli & Wang, Chuang & Xing, Ziwen & Duan, Kaiwen, 2024. "Optimal dynamic thermal management for data center via soft actor-critic algorithm with dynamic control interval and combined-value state space," Applied Energy, Elsevier, vol. 373(C).
- Zeng, Lanting & Qiu, Dawei & Sun, Mingyang, 2022. "Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks," Applied Energy, Elsevier, vol. 324(C).
- Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
- Qiu, Dawei & Dong, Zihang & Zhang, Xi & Wang, Yi & Strbac, Goran, 2022. "Safe reinforcement learning for real-time automatic control in a smart energy-hub," Applied Energy, Elsevier, vol. 309(C).
- Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
- Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 318(C).
- Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
- Zhang, Yiwen & Lin, Rui & Mei, Zhen & Lyu, Minghao & Jiang, Huaiguang & Xue, Ying & Zhang, Jun & Gao, David Wenzhong, 2024. "Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties," Applied Energy, Elsevier, vol. 376(PA).
- Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
- Anand Krishnan Prakash & Kun Zhang & Pranav Gupta & David Blum & Marc Marshall & Gabe Fierro & Peter Alstone & James Zoellick & Richard Brown & Marco Pritoni, 2020. "Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids," Energies, MDPI, vol. 13(12), pages 1-27, June.
- 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.
- Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
- Hindreen Rashid Abdulqadir & Adnan Mohsin Abdulazeez, 2021. "Reinforcement Learning and Modeling Techniques: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 174-189.
- Liu, Shuo & Liu, Xiaohua & Zhang, Tao & Wang, Chaoliang & Liu, Wei, 2024. "Joint optimization for temperature and humidity independent control system based on multi-agent reinforcement learning with cooperative mechanisms," Applied Energy, Elsevier, vol. 375(C).
- Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
- Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
- 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).
- Zhou, Jianhao & Liu, Jun & Xue, Yuan & Liao, Yuhui, 2022. "Total travel costs minimization strategy of a dual-stack fuel cell logistics truck enhanced with artificial potential field and deep reinforcement learning," Energy, Elsevier, vol. 239(PA).
- Lee, Kuan-Cheng & Yang, Hong-Tzer & Tang, Wenjun, 2022. "Data-driven online interactive bidding strategy for demand response," Applied Energy, Elsevier, vol. 319(C).
- Arroyo, Javier & Manna, Carlo & Spiessens, Fred & Helsen, Lieve, 2022. "Reinforced model predictive control (RL-MPC) for building energy management," Applied Energy, Elsevier, vol. 309(C).
- Hu, Rong & Zhou, Kaile & Yin, Hui, 2024. "Reinforcement learning model for incentive-based integrated demand response considering demand-side coupling," Energy, Elsevier, vol. 308(C).
- Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Tian, Ning & Zhao, Wei, 2023. "Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility," Applied Energy, Elsevier, vol. 348(C).
- Zhong, Shengyuan & Wang, Xiaoyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Wang, Yongzhen & Deng, Shuai & Zhu, Jiebei, 2021. "Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating," Applied Energy, Elsevier, vol. 288(C).
- Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).
- Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
- Eduardo J. Salazar & Mauro Jurado & Mauricio E. Samper, 2023. "Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
- Magdalena Krystyna Wyrwicka & Ewa Więcek-Janka & Łukasz Brzeziński, 2023. "Transition to Sustainable Energy System for Smart Cities—Literature Review," Energies, MDPI, vol. 16(21), pages 1-26, October.
- Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
- Touzani, Samir & Prakash, Anand Krishnan & Wang, Zhe & Agarwal, Shreya & Pritoni, Marco & Kiran, Mariam & Brown, Richard & Granderson, Jessica, 2021. "Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency," Applied Energy, Elsevier, vol. 304(C).
- Aya Amer & Khaled Shaban & Ahmed Massoud, 2022. "Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes," Energies, MDPI, vol. 15(21), pages 1-20, November.
- Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
- 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).
- Davide Fop & Ali Reza Yaghoubi & Alfonso Capozzoli, 2024. "Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator," Energies, MDPI, vol. 17(20), pages 1-20, October.
- Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
- Prevedello, Giulio & Werth, Annette, 2021. "The benefits of sharing in off-grid microgrids: A case study in the Philippines," Applied Energy, Elsevier, vol. 303(C).
- Svetozarevic, B. & Baumann, C. & Muntwiler, S. & Di Natale, L. & Zeilinger, M.N. & Heer, P., 2022. "Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: Simulations and experiments," Applied Energy, Elsevier, vol. 307(C).
- Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2019. "Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System," Energies, MDPI, vol. 12(9), pages 1-17, May.
- Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
- Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
- Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
- Yin, Xin & Chen, Haoyong & Liang, Zipeng & Zhu, Yanjin, 2023. "A Flexibility-oriented robust transmission expansion planning approach under high renewable energy resource penetration," Applied Energy, Elsevier, vol. 351(C).
- Ye, Jin & Wang, Xianlian & Hua, Qingsong & Sun, Li, 2024. "Deep reinforcement learning based energy management of a hybrid electricity-heat-hydrogen energy system with demand response," Energy, Elsevier, vol. 305(C).
- Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
- Ruiqiu Yao & Yukun Hu & Liz Varga, 2023. "Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review," Energies, MDPI, vol. 16(5), pages 1-36, March.
- Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
- Vašak, Mario & Banjac, Anita & Hure, Nikola & Novak, Hrvoje & Kovačević, Marko, 2023. "Predictive control based assessment of building demand flexibility in fixed time windows," Applied Energy, Elsevier, vol. 329(C).
- Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- 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).
- Michael Bachseitz & Muhammad Sheryar & David Schmitt & Thorsten Summ & Christoph Trinkl & Wilfried Zörner, 2024. "PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach," Energies, MDPI, vol. 17(8), pages 1-16, April.
- Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
- Rocchetta, R. & Bellani, L. & Compare, M. & Zio, E. & Patelli, E., 2019. "A reinforcement learning framework for optimal operation and maintenance of power grids," Applied Energy, Elsevier, vol. 241(C), pages 291-301.
- Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
- Carlos Cruz & Esther Palomar & Ignacio Bravo & Alfredo Gardel, 2020. "Cooperative Demand Response Framework for a Smart Community Targeting Renewables: Testbed Implementation and Performance Evaluation," Energies, MDPI, vol. 13(11), pages 1-20, June.
- Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
- Xiao, Boyi & Yang, Weiwei & Wu, Jiamin & Walker, Paul D. & Zhang, Nong, 2022. "Energy management strategy via maximum entropy reinforcement learning for an extended range logistics vehicle," Energy, Elsevier, vol. 253(C).
- Tan, Yingqi & Xu, Jingyi & Ma, Junyi & Li, Zirui & Chen, Huiyan & Xi, Junqiang & Liu, Haiou, 2024. "A transferable perception-guided EMS for series hybrid electric unmanned tracked vehicles," Energy, Elsevier, vol. 306(C).
- Rajat Gupta & Sahar Zahiri & Johanna Morey, 2023. "Enhancing User Engagement in Local Energy Initiatives Using Smart Local Energy Engagement Tools: A Meta Study," Energies, MDPI, vol. 16(7), pages 1-25, March.
- 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).
- Ajagekar, Akshay & Decardi-Nelson, Benjamin & You, Fengqi, 2024. "Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 355(C).
- Langer, Lissy & Volling, Thomas, 2022. "A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 327(C).
- Amin, Amin & Kem, Oudom & Gallegos, Pablo & Chervet, Philipp & Ksontini, Feirouz & Mourshed, Monjur, 2022. "Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation," Applied Energy, Elsevier, vol. 305(C).
- Ottavia Valentini & Nikoleta Andreadou & Paolo Bertoldi & Alexandre Lucas & Iolanda Saviuc & Evangelos Kotsakis, 2022. "Demand Response Impact Evaluation: A Review of Methods for Estimating the Customer Baseline Load," Energies, MDPI, vol. 15(14), pages 1-36, July.
- Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
- 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).
- Thibaut Th'eate & Antonio Sutera & Damien Ernst, 2023. "Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis," Papers 2301.11587, arXiv.org.
- Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
- Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.
- Gong, Xun & Wang, Xiaozhe & Cao, Bo, 2023. "On data-driven modeling and control in modern power grids stability: Survey and perspective," Applied Energy, Elsevier, vol. 350(C).
- Ribó-Pérez, D. & Carrión, A. & Rodríguez García, J. & Álvarez Bel, C., 2021. "Ex-post evaluation of Interruptible Load programs with a system optimisation perspective," Applied Energy, Elsevier, vol. 303(C).
- Yi Kuang & Xiuli Wang & Hongyang Zhao & Yijun Huang & Xianlong Chen & Xifan Wang, 2020. "Agent-Based Energy Sharing Mechanism Using Deep Deterministic Policy Gradient Algorithm," Energies, MDPI, vol. 13(19), pages 1-20, September.
- Ajagekar, Akshay & You, Fengqi, 2024. "Variational quantum circuit based demand response in buildings leveraging a hybrid quantum-classical strategy," Applied Energy, Elsevier, vol. 364(C).
- Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
- 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).
- Zheng, Lingwei & Wu, Hao & Guo, Siqi & Sun, Xinyu, 2023. "Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy," Energy, Elsevier, vol. 277(C).
- Lu, Renzhi & Li, Yi-Chang & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2020. "Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management," Applied Energy, Elsevier, vol. 276(C).
- Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.
- Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
- Zhang, Xiongfeng & Lu, Renzhi & Jiang, Junhui & Hong, Seung Ho & Song, Won Seok, 2021. "Testbed implementation of reinforcement learning-based demand response energy management system," Applied Energy, Elsevier, vol. 297(C).
- Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
- Zhang, Xiaohai & Ramírez-Mendiola, José Luis & Li, Mingtao & Guo, Liejin, 2022. "Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study," Applied Energy, Elsevier, vol. 308(C).
- Rae-Jun Park & Kyung-Bin Song & Bo-Sung Kwon, 2020. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
- Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2020. "Introducing reinforcement learning to the energy system design process," Applied Energy, Elsevier, vol. 262(C).
- Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
- Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
- Xiang, Liu, 2020. "Energy emergency supply chain collaboration optimization with group consensus through reinforcement learning considering non-cooperative behaviours," Energy, Elsevier, vol. 210(C).
- Esmaeili Aliabadi, Danial & Chan, Katrina, 2022. "The emerging threat of artificial intelligence on competition in liberalized electricity markets: A deep Q-network approach," Applied Energy, Elsevier, vol. 325(C).
- 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.
- Philipp Wohlgenannt & Sebastian Hegenbart & Elias Eder & Mohan Kolhe & Peter Kepplinger, 2024. "Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach," Energies, MDPI, vol. 17(24), pages 1-19, December.
- Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
- Chenhui Xu & Yunkai Huang, 2023. "Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach," Energies, MDPI, vol. 16(12), pages 1-19, June.
- 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).
- Sun, Guoxin & Yu, Yongheng & Yu, Qihui & Tan, Xin & Wu, Linfeng & Qin, Ripeng & Wang, Yahui, 2024. "Enhanced temperature regulation in compound heating systems: Leveraging guided policy search and model predictive control," Renewable Energy, Elsevier, vol. 236(C).
- Haider, Rabab & Annaswamy, Anuradha M., 2022. "A hybrid architecture for volt-var control in active distribution grids," Applied Energy, Elsevier, vol. 312(C).
- Correa-Jullian, Camila & López Droguett, Enrique & Cardemil, José Miguel, 2020. "Operation scheduling in a solar thermal system: A reinforcement learning-based framework," Applied Energy, Elsevier, vol. 268(C).