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Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression

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  • Bu, Xiangya
  • Wu, Qiuwei
  • Zhou, Bin
  • Li, Canbing

Abstract

Accurate short-term load forecasting (STLF) is essential to improve secure and economic operation of power systems. In this paper, a hybrid STLF model using the conditional generative adversarial network (CGAN) with convolutional neural network (CNN) and semi-supervised regression is proposed to improve the accuracy of STLF. Firstly, a conditional label matrix with relevant factors is constructed as the conditional labels of CGAN. The grey weighted correlation method is applied to generate high-quality similar days as one of the labels. The input data with conditional labels and load time series are decomposed into several sub-modes by the variational mode decomposition (VMD), which transforms the load forecasting into several sub-forecasting. Then, the CGAN generator is to capture the internal feature of each mode with the CNN and generate fake samples, while the CGAN discriminator is modified with a semi-supervised regression layer to extract the nonlinear and dynamic behaviors of the dataset and perform precise STLF. The final forecasting results are obtained by aggregating the results of all sub-mode. The generator and discriminator of the CGAN form a min–max game to improve the sample generation ability and reduce forecasting errors. The simulation results show that the STLF accuracy with the proposed model is significantly improved.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:338:y:2023:i:c:s0306261923002842
    DOI: 10.1016/j.apenergy.2023.120920
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    References listed on IDEAS

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    1. Taiyong Li & Zijie Qian & Ting He, 2020. "Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM," Complexity, Hindawi, vol. 2020, pages 1-20, February.
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    2. Yang, Weijia & Sparrow, Sarah N. & Wallom, David C.H., 2024. "A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods," Applied Energy, Elsevier, vol. 368(C).
    3. 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).
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    5. Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023. "A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective," Energy, Elsevier, vol. 283(C).

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