Short-term Load Forecasting Based on Similar Day and Multi-model Fusion

被引：0

作者：

Zhang D. ^{[1
]}

Sun K. ^{[1
]}

He J. ^{[1
]}

机构：

[1] School of Electrical Engineering, Beijing Jiaotong University, Haidian District, Beijing

来源：

Dianwang Jishu/Power System Technology | 2023年 / 47卷 / 05期

关键词：

CEEMD; ELM; LSTNet; short-term load forecasting; similar days;

D O I：

10.13335/j.1000-3673.pst.2022.1110

中图分类号：

学科分类号：

摘要：

In order to improve the accuracy of load forecasting, a short-term load forecasting framework based on the similar days and multi-model fusion is constructed. Firstly, the key meteorological influencing factors of the load are determined by using the Pearson correlation coefficient, and the similar days are selected by combining the meteorological factors and the load date types to construct the historical data set; Secondly, the Complementary Ensemble Empirical Mode Decomposition (CEEMD) technique is used to decompose the historical data set into the IMF components at different frequencies; Then, the Long/Short-term Time-series Network (LSTNet) and the Extreme Learning Machine (ELM) models are used to predict the high-frequency and low-frequency IMF components respectively, and the final load forecasting results are obtained through the fusion of the results. Finally, through the actual power load verification, the proposed model achieves high prediction accuracy. © 2023 Power System Technology Press. All rights reserved.

引用

页码：1961 / 1969

页数：8

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