Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting

Moving holiday is a non-fixed holiday according to the Gregorian calendar. Most of the electricity load demand studies showed that this event affects the accuracy of load forecasting. It is due to limited recent historical data about moving holiday, a longer time series is acquired to reveal the pat...

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Main Authors: Mansor, R., Kasim, M.M., Othman, M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23188 /
Published: Institute of Advanced Scientific Research, Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084356875&doi=10.5373%2fJARDCS%2fV12SP2%2fSP20201122&partnerID=40&md5=74edeaee9c1dae6fd8c1f09c7282ab35
http://eprints.utp.edu.my/23188/
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spelling utp-eprints.231882021-08-19T06:09:45Z Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting Mansor, R. Kasim, M.M. Othman, M. Moving holiday is a non-fixed holiday according to the Gregorian calendar. Most of the electricity load demand studies showed that this event affects the accuracy of load forecasting. It is due to limited recent historical data about moving holiday, a longer time series is acquired to reveal the pattern, different characteristics of each moving holiday and existence of great amount of irregularities in the load data. All these matters contributed to the uncertainty in load demand forecasting. This paper modifies the classical fuzzy time series (FTS) algorithm by applying weighted subsethood based algorithm (WSBA) in FTS algorithm using segmented electricity load demand time series data. The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of three main phases; data pre-processing, forecasting based on WeSuSFTS model and model evaluation. Step by step explanation for each phase is also presented. The results show that the WeSuSFTS algorithm can be one alternative electricity moving holiday load demand forecasting method and the algorithm achieves its lowest mean absolute percentage error (MAPE) at 2.8 only. © 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. Institute of Advanced Scientific Research, Inc. 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084356875&doi=10.5373%2fJARDCS%2fV12SP2%2fSP20201122&partnerID=40&md5=74edeaee9c1dae6fd8c1f09c7282ab35 Mansor, R. and Kasim, M.M. and Othman, M. (2020) Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting. Journal of Advanced Research in Dynamical and Control Systems, 12 (Specia). pp. 686-700. http://eprints.utp.edu.my/23188/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Moving holiday is a non-fixed holiday according to the Gregorian calendar. Most of the electricity load demand studies showed that this event affects the accuracy of load forecasting. It is due to limited recent historical data about moving holiday, a longer time series is acquired to reveal the pattern, different characteristics of each moving holiday and existence of great amount of irregularities in the load data. All these matters contributed to the uncertainty in load demand forecasting. This paper modifies the classical fuzzy time series (FTS) algorithm by applying weighted subsethood based algorithm (WSBA) in FTS algorithm using segmented electricity load demand time series data. The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of three main phases; data pre-processing, forecasting based on WeSuSFTS model and model evaluation. Step by step explanation for each phase is also presented. The results show that the WeSuSFTS algorithm can be one alternative electricity moving holiday load demand forecasting method and the algorithm achieves its lowest mean absolute percentage error (MAPE) at 2.8 only. © 2020, Institute of Advanced Scientific Research, Inc. All rights reserved.
format Article
author Mansor, R.
Kasim, M.M.
Othman, M.
spellingShingle Mansor, R.
Kasim, M.M.
Othman, M.
Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
author_sort Mansor, R.
title Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
title_short Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
title_full Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
title_fullStr Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
title_full_unstemmed Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
title_sort weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
publisher Institute of Advanced Scientific Research, Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084356875&doi=10.5373%2fJARDCS%2fV12SP2%2fSP20201122&partnerID=40&md5=74edeaee9c1dae6fd8c1f09c7282ab35
http://eprints.utp.edu.my/23188/
_version_ 1741196635542650880
score 11.62408

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