Research on Ultra-Short-Term Load Forecasting Based on Real-Time Electricity Price and Window-Based XGBoost Model

Xin Zhao, Qiushuang Li, Wanlei Xue, Yihang Zhao (), Huiru Zhao and Sen Guo
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Xin Zhao: Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250022, China
Qiushuang Li: Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250022, China
Wanlei Xue: Economic and Technological Research Institute of State Grid Shandong Electric Power Company, Jinan 250022, China
Yihang Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Huiru Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Sen Guo: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Energies, 2022, vol. 15, issue 19, 1-11

Abstract: With the continuous development of new power systems, the load demand on the user side is becoming more and more diverse and random, which also brings difficulties in the accurate prediction of power load. Although the introduction of deep learning algorithms has improved the prediction accuracy to a certain extent, it also faces problems such as large data requirements and low computing efficiency. An ultra-short-term load forecasting method based on the windowed XGBoost model is proposed, which not only reduces the complexity of the model, but also helps the model to capture the autocorrelation effect of the forecast object. At the same time, the real-time electricity price is introduced into the model to improve its forecast accuracy. By simulating the load data of Singapore’s electricity market, it is proved that the proposed model has fewer errors than other deep learning algorithms, and the introduction of the real-time electricity price helps to improve the prediction accuracy of the model. Furthermore, the broad applicability of the proposed method is verified by a sensitivity analysis on data with different sample sizes.

Keywords: window-based XGBoost model; real-time electricity price; ultra-short-term load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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