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

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

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 in...

Full description

Saved in:
Bibliographic Details
Main Authors: Li, Song, Goel, Lalit, Wang, Peng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2017
Subjects:
Ensemble method
Extreme learning machine
Online Access:https://hdl.handle.net/10356/84957
http://hdl.handle.net/10220/42087
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-84957
record_format dspace
spelling sg-ntu-dr.10356-849572020-03-07T13:57:22Z An ensemble approach for short-term load forecasting by extreme learning machine Li, Song Goel, Lalit Wang, Peng School of Electrical and Electronic Engineering Ensemble method Extreme learning machine 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. Accepted version 2017-02-10T08:21:48Z 2019-12-06T15:54:21Z 2017-02-10T08:21:48Z 2019-12-06T15:54:21Z 2016 Journal Article Li, S., Goel, L., & Wang, P. (2016). An ensemble approach for short-term load forecasting by extreme learning machine. Applied Energy, 170, 22-29. 0306-2619 https://hdl.handle.net/10356/84957 http://hdl.handle.net/10220/42087 10.1016/j.apenergy.2016.02.114 en Applied Energy © 2016 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Applied Energy, Elsevier Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.apenergy.2016.02.114]. 25 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Ensemble method
Extreme learning machine
spellingShingle Ensemble method
Extreme learning machine
Li, Song
Goel, Lalit
Wang, Peng
An ensemble approach for short-term load forecasting by extreme learning machine
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Song
Goel, Lalit
Wang, Peng
format Article
author Li, Song
Goel, Lalit
Wang, Peng
author_sort Li, Song
title An ensemble approach for short-term load forecasting by extreme learning machine
title_short An ensemble approach for short-term load forecasting by extreme learning machine
title_full An ensemble approach for short-term load forecasting by extreme learning machine
title_fullStr An ensemble approach for short-term load forecasting by extreme learning machine
title_full_unstemmed An ensemble approach for short-term load forecasting by extreme learning machine
title_sort ensemble approach for short-term load forecasting by extreme learning machine
publishDate 2017
url https://hdl.handle.net/10356/84957
http://hdl.handle.net/10220/42087
_version_ 1681035034605125632

Similar Items