IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v299y2024ics0360544224010314.html

A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network

Author

Listed:
  • Xu, Huifeng
  • Hu, Feihu
  • Liang, Xinhao
  • Zhao, Guoqing
  • Abugunmi, Mohammad

Abstract

Electricity load exhibits daily and weekly cyclical patterns as well as random characteristics. At present, prevailing deep learning models cannot learn electricity load cyclical and stochastic features adequately. This results in insufficient prediction accuracy and the scalability of current methods. To tackle these difficulties, this paper proposes a framework for electrical load prediction based on an Attention Mechanism Time Series Depthwise Separable Convolutional Neural Network (ELPF-ATDSCN). The framework starts by using the Maximum Information Coefficient for exogenous variable selection. It then incorporates a seasonal decomposition algorithm with manual feature engineering to extract the cyclical and stochastic features of the electrical load. Subsequently, the framework employs the ATDSCN to learn the cyclical and stochastic features of the electrical load. In addition, the Bayesian algorithm optimizes model hyperparameters for optimal model performance. Experimental results of point and interval load prediction on datasets from the US and Nordic power markets reveal that the ATDSCN model proposed in this paper enhances load prediction accuracy compared with other models. It can provide more reliable predictions for power system operation and dispatch.

Suggested Citation

  • Xu, Huifeng & Hu, Feihu & Liang, Xinhao & Zhao, Guoqing & Abugunmi, Mohammad, 2024. "A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network," Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:energy:v:299:y:2024:i:c:s0360544224010314
    DOI: 10.1016/j.energy.2024.131258
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224010314
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131258?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Bu, Xiangya & Wu, Qiuwei & Zhou, Bin & Li, Canbing, 2023. "Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression," Applied Energy, Elsevier, vol. 338(C).
    2. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
    3. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    4. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
    5. He, Yaoyao & Cao, Chaojin & Wang, Shuo & Fu, Hong, 2022. "Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems," Applied Energy, Elsevier, vol. 322(C).
    6. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    7. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    8. Zhang, Shu & Wang, Yi & Zhang, Yutian & Wang, Dan & Zhang, Ning, 2020. "Load probability density forecasting by transforming and combining quantile forecasts," Applied Energy, Elsevier, vol. 277(C).
    9. Shi, Jiaqi & Li, Chenxi & Yan, Xiaohe, 2023. "Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization," Energy, Elsevier, vol. 262(PB).
    10. 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).
    11. Wang, Jianzhou & Gao, Jialu & Wei, Danxiang, 2022. "Electric load prediction based on a novel combined interval forecasting system," Applied Energy, Elsevier, vol. 322(C).
    12. Tian, Zhirui & Liu, Weican & Jiang, Wenqian & Wu, Chenye, 2024. "CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability," Energy, Elsevier, vol. 293(C).
    13. Li, Chuang & Li, Guojie & Wang, Keyou & Han, Bei, 2022. "A multi-energy load forecasting method based on parallel architecture CNN-GRU and transfer learning for data deficient integrated energy systems," Energy, Elsevier, vol. 259(C).
    14. Wang, Kang & Wang, Jianzhou & Zeng, Bo & Lu, Haiyan, 2022. "An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
    15. Yin, Linfei & Xie, Jiaxing, 2021. "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems," Applied Energy, Elsevier, vol. 283(C).
    16. Zhang, Dongxue & Wang, Shuai & Liang, Yuqiu & Du, Zhiyuan, 2023. "A novel combined model for probabilistic load forecasting based on deep learning and improved optimizer," Energy, Elsevier, vol. 264(C).
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Xuezhao Zhang & Zijie Chen & Wenxiao Wang & Xiaofen Fang, 2024. "Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network," Energies, MDPI, vol. 17(12), pages 1-21, June.
    2. Yin, Linfei & Ge, Wei, 2024. "Mobileception-ResNet for transient stability prediction of novel power systems," Energy, Elsevier, vol. 309(C).
    3. Li, Chun & Shi, Jiarong, 2025. "A novel CNN-LSTM-based forecasting model for household electricity load by merging mode decomposition, self-attention and autoencoder," Energy, Elsevier, vol. 330(C).
    4. 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.
    5. Zhang, Zeguo & Yin, Jianchuan, 2025. "A novel depthwise separable U-Net for large-scale wave field prediction," Renewable Energy, Elsevier, vol. 254(C).
    6. Furlong, Aidan & Alsafadi, Farah & Palmtag, Scott & Godfrey, Andrew & Wu, Xu, 2025. "Data-driven prediction and uncertainty quantification of PWR crud-induced power shift using convolutional neural networks," Energy, Elsevier, vol. 316(C).
    7. Zhang, Zeguo & Yin, Jianchuan, 2025. "Incremental principal component analysis based depthwise separable Unet model for complex wind system forecasting," Energy, Elsevier, vol. 334(C).
    8. Chengfei Qi & Yanli Feng & Junling Wan & Xinying Mao & Peisen Yuan, 2025. "SFPFMformer: Short-Term Power Load Forecasting for Proxy Electricity Purchase Based on Feature Optimization and Multiscale Decomposition," Mathematics, MDPI, vol. 13(10), pages 1-22, May.
    9. Wang, Yong & Fan, Neng & Wen, Shixiong & Kuang, Wenyu & Yang, Zhongsen & Xiao, Wenlian & Li, Hong-Li & Narayanan, Govindasami & Sapnken, Flavian Emmanuel, 2025. "A novel structural adaptive discrete grey Euler model and its application in clean energy production and consumption," Energy, Elsevier, vol. 323(C).
    10. Wenting Zhao & Haoran Xu & Peng Chen & Juan Zhang & Jing Li & Tingting Cai, 2025. "Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting," Energies, MDPI, vol. 18(13), pages 1-25, June.
    11. Yin, Linfei & Ju, Linyi, 2025. "ShuffleTransformerMulti-headAttentionNet network for user load forecasting," Energy, Elsevier, vol. 322(C).

    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.
    1. Zhao, Xiaoyu & Duan, Pengfei & Cao, Xiaodong & Xue, Qingwen & Zhao, Bingxu & Hu, Jinxue & Zhang, Chenyang & Yuan, Xiaoyang, 2025. "A probabilistic load forecasting method for multi-energy loads based on inflection point optimization and integrated feature screening," Energy, Elsevier, vol. 327(C).
    2. Liu, Tianhao & Li, Fangning & Zhang, Dongdong & Shan, Linke & Zhu, Hongyu & Du, Pengcheng & Jiang, Meihui & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Kong, Fannie, 2026. "Intelligent load forecasting technologies for diverse scenarios in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
    3. Tian, Zhirui & Liu, Weican & Jiang, Wenqian & Wu, Chenye, 2024. "CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability," Energy, Elsevier, vol. 293(C).
    4. Yang, Yi & Xing, Qianyi & Wang, Kang & Li, Caihong & Wang, Jianzhou & Huang, Xiaojia, 2024. "A novel combined probabilistic load forecasting system integrating hybrid quantile regression and knee improved multi-objective optimization strategy," Applied Energy, Elsevier, vol. 356(C).
    5. Duan, Pengfei & Zhao, Xiaoyu & Hu, Jinxue & Li, Kang & Xue, Qingwen & Cao, Xiaodong & Wang, Yanmin & Zhao, Bingxu & Zhang, Chenyang & Yuan, Xiaoyang, 2026. "Multi-energy load forecasting incorporating AI algorithms: research status and trends in integrated energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 229(C).
    6. Giancarlo Aquila & Lucas Barros Scianni Morais & Victor Augusto Durães de Faria & José Wanderley Marangon Lima & Luana Medeiros Marangon Lima & Anderson Rodrigo de Queiroz, 2023. "An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience," Energies, MDPI, vol. 16(21), pages 1-35, November.
    7. Wang, Danhao & Peng, Daogang & Huang, Dongmei & Zhao, Huirong & Qu, Bogang, 2025. "MMEMformer: A multi-scale memory-enhanced transformer framework for short-term load forecasting in integrated energy systems," Energy, Elsevier, vol. 322(C).
    8. Hu, Rong & Zhou, Kaile & Lu, Xinhui, 2025. "Integrated loads forecasting with absence of crucial factors," Energy, Elsevier, vol. 322(C).
    9. Fan, Jingmin & Zhong, Mingwei & Guan, Yuanpeng & Yi, Siqi & Xu, Cancheng & Zhai, Yanpeng & Zhou, Yongwang, 2024. "An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration," Energy, Elsevier, vol. 299(C).
    10. Yin, Linfei & Ju, Linyi, 2025. "ShuffleTransformerMulti-headAttentionNet network for user load forecasting," Energy, Elsevier, vol. 322(C).
    11. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Long-term price guidance mechanism for integrated energy systems based on gated recurrent unit - vision transformer prediction and fractional-order stochastic dynamic calculus control," Energy, Elsevier, vol. 312(C).
    12. Ren, Xiaoxiao & Tian, Xin & Wang, Kai & Yang, Sifan & Chen, Weixiong & Wang, Jinshi, 2025. "Enhanced load forecasting for distributed multi-energy system: A stacking ensemble learning method with deep reinforcement learning and model fusion," Energy, Elsevier, vol. 319(C).
    13. Xinjie Shi & Jianzhou Wang & Jialu Gao, 2025. "Multimodal Optimization Forecasting Model Based on Intelligent Fuzzy Interval Reconstruction," SN Operations Research Forum, Springer, vol. 6(3), pages 1-37, September.
    14. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    15. Tan, Quanwei & Zhu, Jiebei & Xue, Guijun & Xie, Wenju, 2025. "A hybrid heat load forecasting model based on multistage decomposition and dynamic adaptive loss function," Energy, Elsevier, vol. 335(C).
    16. Xing, Qianyi & Huang, Xiaojia & Wang, Kang & Wang, Jianzhou & Wang, Shuai, 2025. "MIG-EWPFS: An ensemble probabilistic wind speed forecasting system integrating multi-dimensional feature extraction, hybrid quantile regression, and Knee improved multi-objective optimization," Energy, Elsevier, vol. 324(C).
    17. Ma, Kai & Nie, Xuefeng & Yang, Jie & Zha, Linlin & Li, Guoqiang & Li, Haibin, 2025. "A power load forecasting method in port based on VMD-ICSS-hybrid neural network," Applied Energy, Elsevier, vol. 377(PB).
    18. Liu, Jingtao & Zhang, Yuxiang & Wang, Yixian & Zhang, Hongbin & Ding, Yunfei, 2026. "A novel multi-model operation mechanism based on Gaussian classification and data decomposition for air-conditioning cooling load prediction in complex public buildings," Energy, Elsevier, vol. 347(C).
    19. Chen, Feng & Deng, Hongyu & Zhang, Xiaoying, 2024. "IG-ENT:A innovative ensemble approach for the flow prediction of main steam system in thermal power plant," Energy, Elsevier, vol. 313(C).
    20. Ye, Xiaoling & Liu, Chengcheng & Xiong, Xiong & Qi, Yinyi, 2025. "Recurrent attention encoder–decoder network for multi-step interval wind power prediction," Energy, Elsevier, vol. 315(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    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:energy:v:299:y:2024:i:c:s0360544224010314. 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.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.