Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques

Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as...

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Bibliographic Details
Main Authors: Xiaogang Huang, Pan Duan, Tingting Guo, Kaigui Xie
Format: Article
Language:English
Published: MDPI AG 2011-01-01
Series:Energies
Subjects:
load forecasting
short-time load
PSO
Online Access:http://www.mdpi.com/1996-1073/4/1/173/
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spelling doaj-7c33a539915f4f5db52888137c49c5942020-11-24T20:49:00ZengMDPI AGEnergies1996-10732011-01-014117318410.3390/en4010173Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering TechniquesXiaogang HuangPan DuanTingting GuoKaigui XieThis paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique according to the degree of similarity of the input samples considering the periodic characteristics of the load. PSO is applied to optimize the model parameters. The complicated nonlinear relationships between the factors influencing the load and the load forecasting can be regressed using the SVR. The practical load data from a city in Chongqing was used to illustrate the proposed method, and the results indicate that the proposed method can obtain higher accuracy compared with the traditional method, and is effective for forecasting the short-term load of power systems. http://www.mdpi.com/1996-1073/4/1/173/load forecastingshort-time loadPSO
collection DOAJ
language English
format Article
sources DOAJ
author Xiaogang Huang
Pan Duan
Tingting Guo
Kaigui Xie
spellingShingle Xiaogang Huang
Pan Duan
Tingting Guo
Kaigui Xie
Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
Energies
load forecasting
short-time load
PSO
author_facet Xiaogang Huang
Pan Duan
Tingting Guo
Kaigui Xie
author_sort Xiaogang Huang
title Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
title_short Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
title_full Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
title_fullStr Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
title_full_unstemmed Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques
title_sort short-term load forecasting for electric power systems using the pso-svr and fcm clustering techniques
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2011-01-01
description This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique according to the degree of similarity of the input samples considering the periodic characteristics of the load. PSO is applied to optimize the model parameters. The complicated nonlinear relationships between the factors influencing the load and the load forecasting can be regressed using the SVR. The practical load data from a city in Chongqing was used to illustrate the proposed method, and the results indicate that the proposed method can obtain higher accuracy compared with the traditional method, and is effective for forecasting the short-term load of power systems.
topic load forecasting
short-time load
PSO
url http://www.mdpi.com/1996-1073/4/1/173/
work_keys_str_mv AT xiaoganghuang shorttermloadforecastingforelectricpowersystemsusingthepsosvrandfcmclusteringtechniques
AT panduan shorttermloadforecastingforelectricpowersystemsusingthepsosvrandfcmclusteringtechniques
AT tingtingguo shorttermloadforecastingforelectricpowersystemsusingthepsosvrandfcmclusteringtechniques
AT kaiguixie shorttermloadforecastingforelectricpowersystemsusingthepsosvrandfcmclusteringtechniques
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