Journal of Shanghai Jiao Tong University ›› 2024, Vol. 58 ›› Issue (6): 819-825.doi: 10.16183/j.cnki.jsjtu.2022.483

• New Type Power System and the Integrated Energy • Previous Articles     Next Articles

TPE-Based Boosting Short-Term Load Forecasting Method

LUO Min1, YANG Jinfeng2, YU Hui2, LAI Yuchen1, GUO Yangyun1, ZHOU Shangli1, XIANG Rui1, TONG Xing3(), CHEN Xiao3   

  1. 1. Digital Grid Research Institute,China Southern Power Grid, Guangzhou 510000, China
    2. China Southern Power Grid Co., Ltd., Guangzhou 510000, China
    3. Shenzhen Orange Intelligence Technology Co., Ltd., Shenzhen 518000, Guangdong, China
  • Received:2022-11-28 Revised:2023-02-17 Accepted:2023-03-09 Online:2024-06-28 Published:2024-07-05

Abstract:

Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning, which is of great significance for power system economic dispatching and safe operation of the system. Many researches on short-term load forecasting using smart models have been conducted at home and abroad. However, how to obtain the optimal structure and parameters accurately and quickly poses a challenge to short-term load forecasting, because the prediction performance of smart forecasting methods is more easily affected by the structure and parameters of the method, and the personality difference of the prediction object itself makes it difficult for the parameters to be reused. Aiming at this problem, a tree-structured Parzen estimator (TPE)-based boosting short-term load forecasting method is proposed. The results show that the proposed method can achieve rapid optimization of structure and parameters, which is verified in the application in short-term load forecasting of a southern province in China to improve the prediction accuracy.

Key words: short-term load forecasting, tree-structured Parzen estimator (TPE), ensemble learning, hyperparameter optimization

CLC Number: