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A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption

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  1. Yin, Linfei & Qiu, Yao, 2022. "Neural network dynamic differential control for long-term price guidance mechanism of flexible energy service providers," Energy, Elsevier, vol. 255(C).
  2. Ghafoori, Mahdi & Abdallah, Moatassem & Kim, Serena, 2023. "Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system," Applied Energy, Elsevier, vol. 340(C).
  3. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
  4. Meihang Zhang & Hua Zhang & Wei Yan & Zhigang Jiang & Shuo Zhu, 2023. "An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
  5. Jian Liu & Xiaotian He & Kangji Li & Wenping Xue, 2025. "A Review of State-of-the-Art AI and Data-Driven Techniques for Load Forecasting," Energies, MDPI, vol. 18(16), pages 1-27, August.
  6. dos Santos Ferreira, Greicili & Martins dos Santos, Deilson & Luciano Avila, Sérgio & Viana Luiz Albani, Vinicius & Cardoso Orsi, Gustavo & Cesar Cordeiro Vieira, Pedro & Nilson Rodrigues, Rafael, 2023. "Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios," Applied Energy, Elsevier, vol. 339(C).
  7. Yongjie Yang & Yulong Li & Yan Cai & Hui Tang & Peng Xu, 2024. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System," Energies, MDPI, vol. 17(15), pages 1-20, July.
  8. Haizhou Fang & Hongwei Tan & Ningfang Dai & Zhaohui Liu & Risto Kosonen, 2023. "Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search," Sustainability, MDPI, vol. 15(9), pages 1-23, May.
  9. Nikseresht, Ali & Amindavar, Hamidreza, 2024. "Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework," Applied Energy, Elsevier, vol. 353(PA).
  10. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
  11. Dai, Jie & Yuan, Qiong & Cai, Helen Huifen & Zhang, Vince & Hasanuzaman, Md. & Selvaraj, J., 2025. "Business-oriented optimization of EV-to-building energy flows: Predictive modeling and scenario evaluation," Energy, Elsevier, vol. 333(C).
  12. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
  13. Yukai Zou & Zhuoxi Chen & Jialiang Guo & Yingsheng Zheng & Xiaolin Yang, 2024. "A data-driven framework for fast building energy demand estimation across future climate conditions," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 19, pages 628-641.
  14. Cao, Wenqiang & Yu, Junqi & Chao, Mengyao & Wang, Jingqi & Yang, Siyuan & Zhou, Meng & Wang, Meng, 2023. "Short-term energy consumption prediction method for educational buildings based on model integration," Energy, Elsevier, vol. 283(C).
  15. Lu, Jie & Zhang, Chaobo & Li, Junyang & Zhao, Yang & Qiu, Weikang & Li, Tingting & Zhou, Kai & He, Jianing, 2022. "Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage," Applied Energy, Elsevier, vol. 322(C).
  16. Du, Han & Zhou, Xinlei & Nord, Natasa & Carden, Yale & Cui, Ping & Ma, Zhenjun, 2025. "Development of a robust data-driven surrogate model to improve energy flexibility of an integrated district heating system with a thermal storage system," Energy, Elsevier, vol. 336(C).
  17. Adinkrah, Julius & Kemausuor, Francis & Tutu Tchao, Eric & Nunoo-Mensah, Henry & Agbemenu, Andrew Selasi & Adu-Poku, Akwasi & Kponyo, Jerry John, 2025. "Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
  18. Ayas Shaqour & Aya Hagishima, 2022. "Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types," Energies, MDPI, vol. 15(22), pages 1-27, November.
  19. Ali, Aliyuda & Aliyuda, Kachalla & Elmitwally, Nouh & Muhammad Bello, Abdulwahab, 2022. "Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage," Applied Energy, Elsevier, vol. 327(C).
  20. Kamil Misiurek & Tadeusz Olkuski & Janusz Zyśk, 2025. "Review of Methods and Models for Forecasting Electricity Consumption," Energies, MDPI, vol. 18(15), pages 1-27, July.
  21. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
  22. Khan, Sheher Yar & Ali, Haider & Mazhar, Abdur Rehman & Ji, Wenjie & Wang, Jihong & Li, Yongliang & Kumar, Mahesh & Shen, Yongliang & Sohrabi, Arvin & Saad ul Haq, Muhammad & Karim, Annas & Liu, Shuli, 2026. "Electrically driven composite phase change materials for latent thermal energy storage in localized space heating: Progress, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 231(C).
  23. Liu, Yanli & Wang, Junyi & Liu, Liqi, 2024. "Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events," Applied Energy, Elsevier, vol. 376(PA).
  24. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
  25. Shang, Jingyi & Gao, Jinfeng & Jiang, Xin & Liu, Mingguang & Liu, Dunnan, 2023. "Optimal configuration of hybrid energy systems considering power to hydrogen and electricity-price prediction: A two-stage multi-objective bi-level framework," Energy, Elsevier, vol. 263(PF).
  26. E. Polosko & O. Holda, 2026. "Optimizing Energy Consumption at MAZ Using IoT Sensors and Neural Networks for Predictive Analysis," Digital Transformation, Educational Establishment “Belarusian State University of Informatics and Radioelectronicsâ€, vol. 32(1).
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