IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v271y2020ics0306261920304578.html

Online pricing of demand response based on long short-term memory and reinforcement learning

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. He, Youmeng & Gu, Chunhua & Gao, Yan & Wang, Jingqi, 2025. "Bi-level day-ahead and real-time hybrid pricing model and its reinforcement learning method," Energy, Elsevier, vol. 322(C).
  2. Zhu, Ying & Xia, Tangbin & Gao, Shuo & Hong, Ge & Pan, Ershun & Xi, Lifeng, 2025. "Integrated maintenance service and spare parts supply strategy for service-oriented manufacturers with partially unknown information," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
  3. 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).
  4. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
  5. Wenhui Zhao & Zilin Wu & Bo Zhou & Jiaoqian Gao, 2024. "Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
  6. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
  7. Hui Wang & Yao Xu, 2024. "Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study," Energies, MDPI, vol. 17(11), pages 1-15, May.
  8. Yan Li & Zhiwen Liu & Chong Shao & Bingjun Lin & Jiayu Rong & Nan Dong & Buyun Su & Yuejia Hong, 2024. "A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control," Energies, MDPI, vol. 17(20), pages 1-14, October.
  9. Guo, Zhilong & Xu, Wei & Yan, Yue & Sun, Mei, 2023. "How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism," Applied Energy, Elsevier, vol. 343(C).
  10. Rong Xia & Jun Dai & Xiangjie Cheng & Jiaqing Fan & Jing Ye & Qiangang Jia & Sijie Chen & Qiang Zhang, 2024. "Demand Response of Integrated Zero-Carbon Power Plant: Model and Method," Energies, MDPI, vol. 17(14), pages 1-15, July.
  11. 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).
  12. 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).
  13. Kong, Xiangyu & Lu, Wenqi & Wu, Jianzhong & Wang, Chengshan & Zhao, Xv & Hu, Wei & Shen, Yu, 2023. "Real-time pricing method for VPP demand response based on PER-DDPG algorithm," Energy, Elsevier, vol. 271(C).
  14. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
  15. 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).
  16. Fabian Lange & Leonard Dreessen & Rainer Schlosser, 2025. "Reinforcement learning versus data-driven dynamic programming: a comparison for finite horizon dynamic pricing markets," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(6), pages 584-600, December.
  17. Yang, Ting & Zhao, Liyuan & Li, Wei & Wu, Jianzhong & Zomaya, Albert Y., 2021. "Towards healthy and cost-effective indoor environment management in smart homes: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 300(C).
  18. Huang, Chunyi & Li, Kangping & Zhang, Ning, 2025. "Strategic joint bidding and pricing of load aggregators in day-ahead demand response market," Applied Energy, Elsevier, vol. 377(PC).
  19. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
  20. Dehghani, Nariman L. & Jeddi, Ashkan B. & Shafieezadeh, Abdollah, 2021. "Intelligent hurricane resilience enhancement of power distribution systems via deep reinforcement learning," Applied Energy, Elsevier, vol. 285(C).
  21. 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).
  22. 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).
  23. Zheng, Shunlin & Qi, Qi & Sun, Yi & Ai, Xin, 2023. "Integrated demand response considering substitute effect and time-varying response characteristics under incomplete information," Applied Energy, Elsevier, vol. 333(C).
  24. 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.
  25. Zhang, Li & Gao, Yan & Zhu, Hongbo & Tao, Li, 2022. "Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach," Energy, Elsevier, vol. 239(PA).
  26. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.