A PSO-SVM-based 24 Hours Power Load Forecasting Model

In order to improve the drawbacks of over-fitting and easily get stuck into local extremes of BACK propagation Neural Network, a new method of combination of wavelet transform and PSO-SVM (Particle Swarm Optimization- Support Vector Machine) power load forecasting model is proposed. By employing wav...

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Bibliographic Details
Main Authors:	Yu Xiaoxu, Ji Haisen
Format:	Article
Language:	English
Published:	EDP Sciences 2015-01-01
Series:	MATEC Web of Conferences
Subjects:	wavelet transform particle swarm optimization support vector machine
Online Access:	http://dx.doi.org/10.1051/matecconf/20152501008

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spelling	doaj-bc44b6369e0440a6988592e29facc7a72021-02-02T00:47:22ZengEDP SciencesMATEC Web of Conferences2261-236X2015-01-01250100810.1051/matecconf/20152501008matecconf_emme2015_01008A PSO-SVM-based 24 Hours Power Load Forecasting ModelYu Xiaoxu0Ji Haisen1State Grid Liaoyang Electric Power Supply CompanyState Grid Liaoyang Electric Power Supply CompanyIn order to improve the drawbacks of over-fitting and easily get stuck into local extremes of BACK propagation Neural Network, a new method of combination of wavelet transform and PSO-SVM (Particle Swarm Optimization- Support Vector Machine) power load forecasting model is proposed. By employing wave-let transform, the authors decompose the time sequences of power load into high-frequency and low-frequency parts, namely the low-frequency part forecast with this model and the high-frequency part forecast with weighted average method. With PSO, which is a heuristic bionic optimization algorithm, the authors figure out the prefer-able parameters of SVM, and the model proposed in this paper is tested to be more accurately to forecast the 24h power load than BP model.http://dx.doi.org/10.1051/matecconf/20152501008wavelet transformparticle swarm optimizationsupport vector machine
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yu Xiaoxu Ji Haisen
spellingShingle	Yu Xiaoxu Ji Haisen A PSO-SVM-based 24 Hours Power Load Forecasting Model MATEC Web of Conferences wavelet transform particle swarm optimization support vector machine
author_facet	Yu Xiaoxu Ji Haisen
author_sort	Yu Xiaoxu
title	A PSO-SVM-based 24 Hours Power Load Forecasting Model
title_short	A PSO-SVM-based 24 Hours Power Load Forecasting Model
title_full	A PSO-SVM-based 24 Hours Power Load Forecasting Model
title_fullStr	A PSO-SVM-based 24 Hours Power Load Forecasting Model
title_full_unstemmed	A PSO-SVM-based 24 Hours Power Load Forecasting Model
title_sort	pso-svm-based 24 hours power load forecasting model
publisher	EDP Sciences
series	MATEC Web of Conferences
issn	2261-236X
publishDate	2015-01-01
description	In order to improve the drawbacks of over-fitting and easily get stuck into local extremes of BACK propagation Neural Network, a new method of combination of wavelet transform and PSO-SVM (Particle Swarm Optimization- Support Vector Machine) power load forecasting model is proposed. By employing wave-let transform, the authors decompose the time sequences of power load into high-frequency and low-frequency parts, namely the low-frequency part forecast with this model and the high-frequency part forecast with weighted average method. With PSO, which is a heuristic bionic optimization algorithm, the authors figure out the prefer-able parameters of SVM, and the model proposed in this paper is tested to be more accurately to forecast the 24h power load than BP model.
topic	wavelet transform particle swarm optimization support vector machine
url	http://dx.doi.org/10.1051/matecconf/20152501008
work_keys_str_mv	AT yuxiaoxu apsosvmbased24hourspowerloadforecastingmodel AT jihaisen apsosvmbased24hourspowerloadforecastingmodel AT yuxiaoxu psosvmbased24hourspowerloadforecastingmodel AT jihaisen psosvmbased24hourspowerloadforecastingmodel
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A PSO-SVM-based 24 Hours Power Load Forecasting Model

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