Building cooling load forecasting of IES considering spatiotemporal coupling based on hybrid deep learning model
Author
Abstract
Suggested Citation
DOI: 10.1016/j.apenergy.2023.121547
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Li, Ao & Xiao, Fu & Zhang, Chong & Fan, Cheng, 2021. "Attention-based interpretable neural network for building cooling load prediction," Applied Energy, Elsevier, vol. 299(C).
- Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
- Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
- Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
- Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
- Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.
- Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
- Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
- Wang, Lei & He, Yigang, 2022. "M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions," Applied Energy, Elsevier, vol. 324(C).
- Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ma, Xin & Peng, Bo & Ma, Xiangxue & Tian, Changbin & Yan, Yi, 2023. "Multi-timescale optimization scheduling of regional integrated energy system based on source-load joint forecasting," Energy, Elsevier, vol. 283(C).
- Zhicheng Xiao & Lijuan Yu & Huajun Zhang & Xuetao Zhang & Yixin Su, 2023. "HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model," Mathematics, MDPI, vol. 11(22), pages 1-24, November.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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).
- Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
- Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
- 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.
- Li, Ao & Xiao, Fu & Zhang, Chong & Fan, Cheng, 2021. "Attention-based interpretable neural network for building cooling load prediction," Applied Energy, Elsevier, vol. 299(C).
- Xu, Lei & Wang, Shengwei & Tang, Rui, 2019. "Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load," Applied Energy, Elsevier, vol. 237(C), pages 180-195.
- Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
- 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).
- Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.
- Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
- Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
- 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).
- Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
- Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
- Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
- 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).
- 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).
- Meng, Anbo & Xie, Zhifeng & Luo, Jianqiang & Zeng, Ying & Xu, Xuancong & Li, Yidian & Wu, Zhenbo & Zhang, Zhan & Zhu, Jianbin & Xian, Zikang & Li, Chen & Yan, Baiping & Yin, Hao, 2023. "An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network," Energy, Elsevier, vol. 282(C).
- Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
- 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.
More about this item
Keywords
Cooling load forecasting; Spatio-temporal coupling; Temporal trend-aware graph attention network; Gate temporal convolutional layer;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:349:y:2023:i:c:s030626192300911x. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.