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Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method

This paper presents a new hybrid load forecast model to improve the accuracy and robustness of load profile forecasting (1-24 hours ahead). It comprises of Wavelet transform and Neural network based on parallel prediction method, which is called 'PWNN'. Wavelet transform is used to decompo...

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Main Authors: Sovann, N., Nallagownden, P., Baharudin, Z.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011961530&doi=10.1109%2fICIAS.2016.7824088&partnerID=40&md5=3b9fe43e85d363ff889cb04587fabcf7
http://eprints.utp.edu.my/20219/
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spelling my.utp.eprints.202192018-04-22T14:46:08Z Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method Sovann, N. Nallagownden, P. Baharudin, Z. This paper presents a new hybrid load forecast model to improve the accuracy and robustness of load profile forecasting (1-24 hours ahead). It comprises of Wavelet transform and Neural network based on parallel prediction method, which is called 'PWNN'. Wavelet transform is used to decompose the original load series into multiple load sub-series with different frequencies. Then, neural network is used to predict each load sub-series using parallel prediction method. The load forecast can be obtained by inverse wavelet transform. The results indicate that PWNN has a significant improvement of accuracy and robustness in load forecasting over other models used for comparison in this study. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011961530&doi=10.1109%2fICIAS.2016.7824088&partnerID=40&md5=3b9fe43e85d363ff889cb04587fabcf7 Sovann, N. and Nallagownden, P. and Baharudin, Z. (2017) Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method. International Conference on Intelligent and Advanced Systems, ICIAS 2016 . http://eprints.utp.edu.my/20219/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This paper presents a new hybrid load forecast model to improve the accuracy and robustness of load profile forecasting (1-24 hours ahead). It comprises of Wavelet transform and Neural network based on parallel prediction method, which is called 'PWNN'. Wavelet transform is used to decompose the original load series into multiple load sub-series with different frequencies. Then, neural network is used to predict each load sub-series using parallel prediction method. The load forecast can be obtained by inverse wavelet transform. The results indicate that PWNN has a significant improvement of accuracy and robustness in load forecasting over other models used for comparison in this study. © 2016 IEEE.
format Article
author Sovann, N.
Nallagownden, P.
Baharudin, Z.
spellingShingle Sovann, N.
Nallagownden, P.
Baharudin, Z.
Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
author_facet Sovann, N.
Nallagownden, P.
Baharudin, Z.
author_sort Sovann, N.
title Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
title_short Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
title_full Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
title_fullStr Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
title_full_unstemmed Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
title_sort electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011961530&doi=10.1109%2fICIAS.2016.7824088&partnerID=40&md5=3b9fe43e85d363ff889cb04587fabcf7
http://eprints.utp.edu.my/20219/
_version_ 1738656180477100032
score 13.211869

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