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Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion

Author

Listed:
  • Qingqing Ji

    (University of Chinese Academy of Sciences, Beijing 100049, China
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
    These authors contributed equally to this work.)

  • Shiyu Zhang

    (School of Information Science & Engineering, Yunnan University, Yunnan 650500, China
    These authors contributed equally to this work.)

  • Qiao Duan

    (Faculty of Humanities and Social Sciences, Beijing University of Technology, Beijing 100124, China)

  • Yuhan Gong

    (Fan Gongxiu Honors College, Beijing University of Technology, Beijing 100124, China)

  • Yaowei Li

    (School of Information Science & Engineering, Yunnan University, Yunnan 650500, China)

  • Xintong Xie

    (Beijing-Dublin International College, Beijing University of Technology, Beijing 100124, China)

  • Jikang Bai

    (Beijing-Dublin International College, Beijing University of Technology, Beijing 100124, China)

  • Chunli Huang

    (Faculty of Science, Beijing University of Technology, Beijing 100124, China)

  • Xu Zhao

    (Faculty of Science, Beijing University of Technology, Beijing 100124, China)

Abstract

With the continuous development of economy and society, power demand forecasting has become an important task of the power industry. Accurate power demand forecasting can promote the operation and development of the power supply industry. However, since power consumption is affected by a number of factors, it is difficult to accurately predict the power demand data. With the accumulation of data in the power industry, machine learning technology has shown great potential in power demand forecasting. In this study, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) are integrated by stacking to build an XLG-LR fusion model to predict power demand. Firstly, preprocessing was carried out on 13 months of electricity and meteorological data. Next, the hyperparameters of each model were adjusted and optimized. Secondly, based on the optimal hyperparameter configuration, a prediction model was built using the training set (70% of the data). Finally, the test set (30% of the data) was used to evaluate the performance of each model. Mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and goodness-of-fit coefficient (R^2) were utilized to analyze each model at different lengths of time, including their seasonal, weekly, and monthly forecast effect. Furthermore, the proposed fusion model was compared with other neural network models such as the GRU, LSTM and TCN models. The results showed that the XLG-LR model achieved the best prediction results at different time lengths, and at the same time consumed the least time compared to the neural network model. This method can provide a more reliable reference for the operation and dispatch of power enterprises and future power construction and planning.

Suggested Citation

  • Qingqing Ji & Shiyu Zhang & Qiao Duan & Yuhan Gong & Yaowei Li & Xintong Xie & Jikang Bai & Chunli Huang & Xu Zhao, 2022. "Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion," Mathematics, MDPI, vol. 10(12), pages 1-30, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2148-:d:843202
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    References listed on IDEAS

    as
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