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Short-term load forecasting method based on fuzzy time series, seasonality and long memory process

Seasonal Auto Regressive Fractionally Integrated Moving Average (SARFIMA) is a well-known model for forecasting of seasonal time series that follow a long memory process. However, to better boost the accuracy of forecasts inside such data for nonlinear problem, in this study, a combination of Fuzzy...

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Main Authors:	Sadaei, Hossein Javedani, Guimaraes, Frederico Gadelha, Cidiney Jose, Da Silva, Lee, Muhammad Hisyam @ Wee Yew, Tayyebe, Eslami
Format:	Article
Published:	Elsevier Science BV 2017
Subjects:	Q Science
Online Access:	http://eprints.utm.my/id/eprint/66169/ http://dx.doi.org/10.1016/j.ijar.2017.01.006
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id	my.utm.66169
record_format	eprints
spelling	my.utm.661692017-07-13T07:18:13Z http://eprints.utm.my/id/eprint/66169/ Short-term load forecasting method based on fuzzy time series, seasonality and long memory process Sadaei, Hossein Javedani Guimaraes, Frederico Gadelha Cidiney Jose, Da Silva Lee, Muhammad Hisyam @ Wee Yew Tayyebe, Eslami Q Science Seasonal Auto Regressive Fractionally Integrated Moving Average (SARFIMA) is a well-known model for forecasting of seasonal time series that follow a long memory process. However, to better boost the accuracy of forecasts inside such data for nonlinear problem, in this study, a combination of Fuzzy Time Series (FTS) with SARFIMA is proposed. To build the proposed model, certain parameters requires to be estimated. Therefore, a reliable Evolutionary Algorithm namely Particle Swarm Optimization (PSO) is employed. As a case study, a seasonal long memory time series, i.e., short term load consumption historical data, is selected. In fact, Short Term Load Forecasting (STLF) plays a key role in energy management systems (EMS) and in the decision making process of every power supply organization. In order to evaluate the proposed method, some experiments, using eight datasets of half-hourly load data from England and France for the year 2005 and four data sets of hourly load data from Malaysia for the year 2007, are designed. Although the focus of this research is STLF, six other seasonal long memory time series from several interesting case studies are employed to better evaluate the performance of the proposed method. The results are compared with some novel FTS methods and new state-of-the-art forecasting methods. The analysis of the results indicates that the proposed method presents higher accuracy than its counterparts, representing an efficient hybrid method for load forecasting problems. Elsevier Science BV 2017-01-04 Article PeerReviewed Sadaei, Hossein Javedani and Guimaraes, Frederico Gadelha and Cidiney Jose, Da Silva and Lee, Muhammad Hisyam @ Wee Yew and Tayyebe, Eslami (2017) Short-term load forecasting method based on fuzzy time series, seasonality and long memory process. International Journal of Approximate Reasoning, 83 . pp. 196-217. ISSN 0888-613X http://dx.doi.org/10.1016/j.ijar.2017.01.006 DOI:10.1016/j.ijar.2017.01.006
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	Q Science
spellingShingle	Q Science Sadaei, Hossein Javedani Guimaraes, Frederico Gadelha Cidiney Jose, Da Silva Lee, Muhammad Hisyam @ Wee Yew Tayyebe, Eslami Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
description	Seasonal Auto Regressive Fractionally Integrated Moving Average (SARFIMA) is a well-known model for forecasting of seasonal time series that follow a long memory process. However, to better boost the accuracy of forecasts inside such data for nonlinear problem, in this study, a combination of Fuzzy Time Series (FTS) with SARFIMA is proposed. To build the proposed model, certain parameters requires to be estimated. Therefore, a reliable Evolutionary Algorithm namely Particle Swarm Optimization (PSO) is employed. As a case study, a seasonal long memory time series, i.e., short term load consumption historical data, is selected. In fact, Short Term Load Forecasting (STLF) plays a key role in energy management systems (EMS) and in the decision making process of every power supply organization. In order to evaluate the proposed method, some experiments, using eight datasets of half-hourly load data from England and France for the year 2005 and four data sets of hourly load data from Malaysia for the year 2007, are designed. Although the focus of this research is STLF, six other seasonal long memory time series from several interesting case studies are employed to better evaluate the performance of the proposed method. The results are compared with some novel FTS methods and new state-of-the-art forecasting methods. The analysis of the results indicates that the proposed method presents higher accuracy than its counterparts, representing an efficient hybrid method for load forecasting problems.
format	Article
author	Sadaei, Hossein Javedani Guimaraes, Frederico Gadelha Cidiney Jose, Da Silva Lee, Muhammad Hisyam @ Wee Yew Tayyebe, Eslami
author_facet	Sadaei, Hossein Javedani Guimaraes, Frederico Gadelha Cidiney Jose, Da Silva Lee, Muhammad Hisyam @ Wee Yew Tayyebe, Eslami
author_sort	Sadaei, Hossein Javedani
title	Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
title_short	Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
title_full	Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
title_fullStr	Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
title_full_unstemmed	Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
title_sort	short-term load forecasting method based on fuzzy time series, seasonality and long memory process
publisher	Elsevier Science BV
publishDate	2017
url	http://eprints.utm.my/id/eprint/66169/ http://dx.doi.org/10.1016/j.ijar.2017.01.006
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score	13.211869

Short-term load forecasting method based on fuzzy time series, seasonality and long memory process

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