An ensemble approach for short-term load forecasting by extreme learning machine

被引：176

作者：

Li, Song ^{[1
]}

Goel, Lalit ^{[1
]}

Wang, Peng ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

APPLIED ENERGY | 2016年 / 170卷

关键词：

Ensemble method; Extreme learning machine; Partial least squares regression; Short-term load forecasting; Wavelet transform; SUPPORT VECTOR REGRESSION; WAVELET TRANSFORM; NEURAL-NETWORKS; MODEL;

D O I：

10.1016/j.apenergy.2016.02.114

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper proposes a novel ensemble method for short-term load forecasting based on wavelet transform, extreme learning machine (ELM) and partial least squares regression. In order to improve forecasting performance, a wavelet-based ensemble strategy is introduced into the forecasting model. The individual forecasters are derived from different combinations of mother wavelet and number of decomposition levels. For each sub-component from the wavelet decomposition, a parallel model consisting of 24 ELMs is invoked to predict the hourly load of the next day. The individual forecasts are then combined to form the ensemble forecast using the partial least squares regression method. Numerical results show that the proposed method can significantly improve forecasting performance. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：22 / 29

页数：8

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