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Attention-based interpretable neural network for building cooling load prediction

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Cited by:

  1. Guo, Yanhua & Wang, Ningbo & Shao, Shuangquan & Huang, Congqi & Zhang, Zhentao & Li, Xiaoqiong & Wang, Youdong, 2024. "A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
  2. Maleki, Neda & Lundström, Oxana & Musaddiq, Arslan & Jeansson, John & Olsson, Tobias & Ahlgren, Fredrik, 2024. "Future energy insights: Time-series and deep learning models for city load forecasting," Applied Energy, Elsevier, vol. 374(C).
  3. 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).
  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. Jia, Lizhi & Liu, Junjie & Chong, Adrian & Dai, Xilei, 2022. "Deep learning and physics-based modeling for the optimization of ice-based thermal energy systems in cooling plants," Applied Energy, Elsevier, vol. 322(C).
  6. Wang, Xinlin & Wang, Hao & Li, Shengping & Jin, Haizhen, 2024. "A reinforcement learning-based online learning strategy for real-time short-term load forecasting," Energy, Elsevier, vol. 305(C).
  7. Donghun Lee & Jongeun Kim & Suhee Kim & Kwanho Kim, 2023. "Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks," Energies, MDPI, vol. 16(24), pages 1-13, December.
  8. Liu, Yiren & Zhao, Xiangyu & Qin, S. Joe, 2024. "Dynamically engineered multi-modal feature learning for predictions of office building cooling loads," Applied Energy, Elsevier, vol. 355(C).
  9. Jue Guo & Chong Zhang, 2022. "Utilization of Window System as Exhaust Air Heat Recovery Device and Its Energy Performance Evaluation: A Comparative Study," Energies, MDPI, vol. 15(9), pages 1-18, April.
  10. Liang, Xinbin & Zhu, Xu & Chen, Kang & Chen, Siliang & Jin, Xinqiao & Du, Zhimin, 2023. "Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems," Energy, Elsevier, vol. 263(PC).
  11. Varlamis, Iraklis & Sardianos, Christos & Chronis, Christos & Dimitrakopoulos, George & Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2022. "Smart fusion of sensor data and human feedback for personalized energy-saving recommendations," Applied Energy, Elsevier, vol. 305(C).
  12. Massimiliano Manfren & Karla M. Gonzalez-Carreon & Patrick A. B. James, 2024. "Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps," Energies, MDPI, vol. 17(4), pages 1-22, February.
  13. Yan, Xiuying & Ji, Xingxing & Meng, Qinglong & Sun, Hang & Lei, Yu, 2024. "A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism," Energy, Elsevier, vol. 292(C).
  14. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
  15. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
  16. 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).
  17. Marta Skiba & Barbara Dutka & Mariusz Młynarczuk, 2021. "MLP-Based Model for Estimation of Methane Seam Pressure," Energies, MDPI, vol. 14(22), pages 1-12, November.
  18. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
  19. Luca Gugliermetti & Fabrizio Cumo & Sofia Agostinelli, 2024. "A Future Direction of Machine Learning for Building Energy Management: Interpretable Models," Energies, MDPI, vol. 17(3), pages 1-27, February.
  20. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
  21. Gao, Yuan & Hu, Zehuan & Shi, Shanrui & Chen, Wei-An & Liu, Mingzhe, 2024. "Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan," Applied Energy, Elsevier, vol. 359(C).
  22. Shao, Junqiang & Huang, Zhiyuan & Chen, Yugui & Li, Depeng & Xu, Xiangguo, 2023. "A practical application-oriented model predictive control algorithm for direct expansion (DX) air-conditioning (A/C) systems that balances thermal comfort and energy consumption," Energy, Elsevier, vol. 269(C).
  23. 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).
  24. 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.
  25. Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).
  26. Yu, Min & Niu, Dongxiao & Zhao, Jinqiu & Li, Mingyu & Sun, Lijie & Yu, Xiaoyu, 2023. "Building cooling load forecasting of IES considering spatiotemporal coupling based on hybrid deep learning model," Applied Energy, Elsevier, vol. 349(C).
  27. Zheng, Peijun & Zhou, Heng & Liu, Jiang & Nakanishi, Yosuke, 2023. "Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture," Applied Energy, Elsevier, vol. 349(C).
  28. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
  29. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
  30. 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).
  31. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
  32. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
  33. Nadia Jahanafroozi & Saman Shokrpour & Fatemeh Nejati & Omrane Benjeddou & Mohammad Worya Khordehbinan & Afshin Marani & Moncef L. Nehdi, 2022. "New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
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