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

Qingqing Ji, Shiyu Zhang, Qiao Duan, Yuhan Gong, Yaowei Li, Xintong Xie, Jikang Bai, Chunli Huang and Xu Zhao
Additional contact information
Qingqing Ji: University of Chinese Academy of Sciences, Beijing 100049, China
Shiyu Zhang: School of Information Science & Engineering, Yunnan University, Yunnan 650500, China
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

Mathematics, 2022, vol. 10, issue 12, 1-30

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.

Keywords: power demand forecasting; model fusion; gradient boosting decision tree (GBDT); extreme gradient boosting (XGBoost); light gradient boosting machine (LightGBM) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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