Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series

We propose a combined method that is based on the fuzzy time series (FTS) and convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in the proposed method, multivariate time series data which include hourly load data, hourly temperature time series and fuzzified ve...

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Main Authors: Sadaei, Hossein Javedani, de Lima e Silva, Petrônio Cândido, Guimarães, Frederico Gadelha, Lee, Muhammad Hisyam
Format: Article
Published: Elsevier Ltd. 2019
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Online Access:http://eprints.utm.my/id/eprint/87565/
http://dx.doi.org/10.1016/j.energy.2019.03.081
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.875652020-11-30T09:03:52Z http://eprints.utm.my/id/eprint/87565/ Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series Sadaei, Hossein Javedani de Lima e Silva, Petrônio Cândido Guimarães, Frederico Gadelha Lee, Muhammad Hisyam QA Mathematics We propose a combined method that is based on the fuzzy time series (FTS) and convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in the proposed method, multivariate time series data which include hourly load data, hourly temperature time series and fuzzified version of load time series, was converted into multi-channel images to be fed to a proposed deep learning CNN model with proper architecture. By using images which have been created from the sequenced values of multivariate time series, the proposed CNN model could determine and extract related important parameters, in an implicit and automatic way, without any need for human interaction and expert knowledge, and all by itself. By following this strategy, it was shown how employing the proposed method is easier than some traditional STLF models. Therefore it could be seen as one of the big difference between the proposed method and some state-of-the-art methodologies of STLF. Moreover, using fuzzy logic had great contribution to control over-fitting by expressing one dimension of time series by a fuzzy space, in a spectrum, and a shadow instead of presenting it with exact numbers. Various experiments on test data-sets support the efficiency of the proposed method. Elsevier Ltd. 2019-05 Article PeerReviewed Sadaei, Hossein Javedani and de Lima e Silva, Petrônio Cândido and Guimarães, Frederico Gadelha and Lee, Muhammad Hisyam (2019) Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy, 175 . pp. 365-377. ISSN 0360-5442 http://dx.doi.org/10.1016/j.energy.2019.03.081
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Sadaei, Hossein Javedani
de Lima e Silva, Petrônio Cândido
Guimarães, Frederico Gadelha
Lee, Muhammad Hisyam
Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
description We propose a combined method that is based on the fuzzy time series (FTS) and convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in the proposed method, multivariate time series data which include hourly load data, hourly temperature time series and fuzzified version of load time series, was converted into multi-channel images to be fed to a proposed deep learning CNN model with proper architecture. By using images which have been created from the sequenced values of multivariate time series, the proposed CNN model could determine and extract related important parameters, in an implicit and automatic way, without any need for human interaction and expert knowledge, and all by itself. By following this strategy, it was shown how employing the proposed method is easier than some traditional STLF models. Therefore it could be seen as one of the big difference between the proposed method and some state-of-the-art methodologies of STLF. Moreover, using fuzzy logic had great contribution to control over-fitting by expressing one dimension of time series by a fuzzy space, in a spectrum, and a shadow instead of presenting it with exact numbers. Various experiments on test data-sets support the efficiency of the proposed method.
format Article
author Sadaei, Hossein Javedani
de Lima e Silva, Petrônio Cândido
Guimarães, Frederico Gadelha
Lee, Muhammad Hisyam
author_facet Sadaei, Hossein Javedani
de Lima e Silva, Petrônio Cândido
Guimarães, Frederico Gadelha
Lee, Muhammad Hisyam
author_sort Sadaei, Hossein Javedani
title Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
title_short Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
title_full Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
title_fullStr Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
title_full_unstemmed Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
title_sort short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series
publisher Elsevier Ltd.
publishDate 2019
url http://eprints.utm.my/id/eprint/87565/
http://dx.doi.org/10.1016/j.energy.2019.03.081
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