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Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks

Author

Listed:
  • Yixing Wang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Meiqin Liu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Zhejing Bao

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Senlin Zhang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Short-term load forecasting is an important task for the planning and reliable operation of power grids. High-accuracy forecasting for individual customers helps to make arrangements for generation and reduce electricity costs. Artificial intelligent methods have been applied to short-term load forecasting in past research, but most did not consider electricity use characteristics, efficiency, and more influential factors. In this paper, a method for short-term load forecasting with multi-source data using gated recurrent unit neural networks is proposed. The load data of customers are preprocessed by clustering to reduce the interference of electricity use characteristics. The environmental factors including date, weather and temperature are quantified to extend the input of the whole network so that multi-source information is considered. Gated recurrent unit neural networks are used for extracting temporal features with simpler architecture and less convergence time in the hidden layers. The detailed results of the real-world experiments are shown by the forecasting curve and mean absolute percentage error to prove the availability and superiority of the proposed method compared to the current forecasting methods.

Suggested Citation

  • Yixing Wang & Meiqin Liu & Zhejing Bao & Senlin Zhang, 2018. "Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks," Energies, MDPI, vol. 11(5), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1138-:d:144422
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    References listed on IDEAS

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    Cited by:

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    2. Seon Hyeog Kim & Gyul Lee & Gu-Young Kwon & Do-In Kim & Yong-June Shin, 2018. "Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting," Energies, MDPI, vol. 11(12), pages 1-17, December.
    3. Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
    4. Yizhen Wang & Ningqing Zhang & Xiong Chen, 2021. "A Short-Term Residential Load Forecasting Model Based on LSTM Recurrent Neural Network Considering Weather Features," Energies, MDPI, vol. 14(10), pages 1-13, May.
    5. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    6. Jiakang Wang & Hui Liu & Guangji Zheng & Ye Li & Shi Yin, 2023. "Short-Term Load Forecasting Based on Outlier Correction, Decomposition, and Ensemble Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-16, May.
    7. Zhaorui Meng & Xianze Xu, 2019. "A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment," Energies, MDPI, vol. 12(24), pages 1-14, December.
    8. Dong, Ming & Shi, Jian & Shi, Qingxin, 2020. "Multi-year long-term load forecast for area distribution feeders based on selective sequence learning," Energy, Elsevier, vol. 206(C).
    9. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.

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