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A hybrid deep transfer learning strategy for short term cross-building energy prediction

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  1. Niu, Wente & Sun, Yuping & Zhang, Xiaowei & Lu, Jialiang & Liu, Hualin & Li, Qiaojing & Mu, Ying, 2023. "An ensemble transfer learning strategy for production prediction of shale gas wells," Energy, Elsevier, vol. 275(C).
  2. Tang, Lingfeng & Xie, Haipeng & Wang, Xiaoyang & Bie, Zhaohong, 2023. "Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach," Applied Energy, Elsevier, vol. 337(C).
  3. Wang, Yuqing & Fu, Wenjie & Wang, Junlong & Zhen, Zhao & Wang, Fei, 2024. "Ultra-short-term distributed PV power forecasting for virtual power plant considering data-scarce scenarios," Applied Energy, Elsevier, vol. 373(C).
  4. Wang, Lijin & Fan, Weipeng & Jiang, Guoqian & Xie, Ping, 2023. "An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms," Energy, Elsevier, vol. 284(C).
  5. Yuan, Yue & Chen, Zhihua & Wang, Zhe & Sun, Yifu & Chen, Yixing, 2023. "Attention mechanism-based transfer learning model for day-ahead energy demand forecasting of shopping mall buildings," Energy, Elsevier, vol. 270(C).
  6. Li, Guannan & Wu, Yubei & Yoon, Sungmin & Fang, Xi, 2024. "Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning," Energy, Elsevier, vol. 299(C).
  7. Shangfu Wei & Xiaoqing Bai, 2022. "Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network," Energies, MDPI, vol. 15(5), pages 1-21, February.
  8. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
  9. Ma, Zhihao & Jiang, Gang & Hu, Yuqing & Chen, Jianli, 2025. "A review of physics-informed machine learning for building energy modeling," Applied Energy, Elsevier, vol. 381(C).
  10. Fanyue Qian & Weijun Gao & Dan Yu & Yongwen Yang & Yingjun Ruan, 2022. "An Analysis of the Potential of Hydrogen Energy Technology on Demand Side Based on a Carbon Tax: A Case Study in Japan," Energies, MDPI, vol. 16(1), pages 1-23, December.
  11. Zhang, Yunfei & Zhou, Zhihua & Du, Yahui & Shen, Jun & Li, Zhenxing & Yuan, Jianjuan, 2023. "A data transfer method based on one dimensional convolutional neural network for cross-building load prediction," Energy, Elsevier, vol. 277(C).
  12. Yong Zhou & Lingyu Wang & Junhao Qian, 2022. "Application of Combined Models Based on Empirical Mode Decomposition, Deep Learning, and Autoregressive Integrated Moving Average Model for Short-Term Heating Load Predictions," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
  13. Chen, Xi & Gu, Bin & Feng, Wentao & Tan, Jingying & Kong, Xiangzhong & Li, Shi & Chen, Yiyu & Wan, Zhongmin, 2024. "Research on control strategy of PEMFC air supply system for power and efficiency improvement," Energy, Elsevier, vol. 304(C).
  14. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  15. Dongsu Kim & Yongjun Lee & Kyungil Chin & Pedro J. Mago & Heejin Cho & Jian Zhang, 2023. "Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
  16. Antonella Yaacoub & Moez Esseghir & Leila Merghem-Boulahia, 2023. "A Review of Different Methodologies to Study Occupant Comfort and Energy Consumption," Energies, MDPI, vol. 16(4), pages 1-18, February.
  17. Chen, Siliang & Ge, Wei & Liang, Xinbin & Jin, Xinqiao & Du, Zhimin, 2024. "Lifelong learning with deep conditional generative replay for dynamic and adaptive modeling towards net zero emissions target in building energy system," Applied Energy, Elsevier, vol. 353(PB).
  18. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Peng, Pei & Li, Wenqiang & Shi, Xing, 2023. "Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level," Energy, Elsevier, vol. 263(PB).
  19. Kim, Dongsu & Seomun, Gu & Lee, Yongjun & Cho, Heejin & Chin, Kyungil & Kim, Min-Hwi, 2024. "Forecasting building energy demand and on-site power generation for residential buildings using long and short-term memory method with transfer learning," Applied Energy, Elsevier, vol. 368(C).
  20. Li, Tao & Liu, Xiangyu & Li, Guannan & Wang, Xing & Ma, Jiangqiaoyu & Xu, Chengliang & Mao, Qianjun, 2024. "A systematic review and comprehensive analysis of building occupancy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
  21. Coraci, Davide & Brandi, Silvio & Hong, Tianzhen & Capozzoli, Alfonso, 2023. "Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings," Applied Energy, Elsevier, vol. 333(C).
  22. Ma, Yichuan X. & Yeung, Lawrence K., 2024. "BEForeGAN: An image-based deep generative approach for day-ahead forecasting of building HVAC energy consumption," Applied Energy, Elsevier, vol. 376(PA).
  23. Xin Zhang & Peng Li, 2023. "Transfer Learning in the Transformer Model for Thermal Comfort Prediction: A Case of Limited Data," Energies, MDPI, vol. 16(20), pages 1-19, October.
  24. Sun, Luning & Hu, Zehuan & Mae, Masayuki & Imaizumi, Taiji, 2025. "Deep transfer learning strategy based on TimesBlock-CDAN for predicting thermal environment and air conditioner energy consumption in residential buildings," Applied Energy, Elsevier, vol. 381(C).
  25. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
  26. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
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