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A Prosumer Power Prediction Method Based on Dynamic Segmented Curve Matching and Trend Feature Perception

Author

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  • Biyun Chen

    (Key Laboratory of Power System Optimization and Energy Saving Technology, Guangxi University, Nanning 530004, China)

  • Qi Xu

    (Key Laboratory of Power System Optimization and Energy Saving Technology, Guangxi University, Nanning 530004, China)

  • Zhuoli Zhao

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Xiaoxuan Guo

    (Electric Power Research Institute, Guangxi Power Grid Corporation, Nanning 530023, China)

  • Yongjun Zhang

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Jingmin Chi

    (Guangxi Minhai Energy Co., Ltd., Nanning 530012, China)

  • Canbing Li

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

With the massive installation of distributed renewable energy (DRE) generation, many prosumers with the dual attributes of load and power supply have emerged. Different DRE permeability and the corresponding peak-valley timing characteristics have an impact on the power features of prosumers, so new models and methods are needed to reflect the new features brought about by these factors. This paper proposes a method for predicting the power of prosumers. In this method, dynamic segmented curve matching is applied to reduce the complexity of source–load coupling features and improve the effectiveness of the input features, and trend feature perception based on a temporal convolutional network (TCN) was applied to grasp the power trend of prosumers by predicting the multisegment trend indexes. The LST-Atten prediction model based on a temporal attention mechanism (TAM) and a long short-term memory (LSTM) network was applied to predict “day-ahead” power, which combines the trend indexes and similar curve sets as the input. Simulation results show that the proposed model has higher accuracy than individual models. Furthermore, the proposed model can maintain prediction stability under different renewable energy permeability scenarios.

Suggested Citation

  • Biyun Chen & Qi Xu & Zhuoli Zhao & Xiaoxuan Guo & Yongjun Zhang & Jingmin Chi & Canbing Li, 2023. "A Prosumer Power Prediction Method Based on Dynamic Segmented Curve Matching and Trend Feature Perception," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3376-:d:1066339
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    References listed on IDEAS

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    Cited by:

    1. Dongdong Zhang & Cunhao Rong & Hui Hwang Goh & Hui Liu & Xiang Li & Hongyu Zhu & Thomas Wu, 2023. "Reform of Electrical Engineering Undergraduate Teaching and the Curriculum System in the Context of the Energy Internet," Sustainability, MDPI, vol. 15(6), pages 1-37, March.
    2. Bożena Gajdzik & Magdalena Jaciow & Radosław Wolniak & Robert Wolny & Wieslaw Wes Grebski, 2023. "Energy Behaviors of Prosumers in Example of Polish Households," Energies, MDPI, vol. 16(7), pages 1-26, March.
    3. Xiaoqing Bai & Chun Wei & Peijie Li & Dongliang Xiao, 2023. "Editorial for the Special Issue on Sustainable Power Systems and Optimization," Sustainability, MDPI, vol. 15(6), pages 1-3, March.

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