Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Author: Sadaei, H. J.
Format: Article
Published: Elsevier Inc. 2017
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/76329/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010953682&doi=10.1016%2fj.ijar.2017.01.006&partnerID=40&md5=8e4794ff52462862570959e801fd7c05
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.76329
record_format eprints
spelling my.utm.763292018-04-12T05:43:54Z http://eprints.utm.my/id/eprint/76329/ Short-term load forecasting method based on fuzzy time series, seasonality and long memory process Sadaei, H. J. QA Mathematics 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 Inc. 2017 Article PeerReviewed Sadaei, H. J. (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 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010953682&doi=10.1016%2fj.ijar.2017.01.006&partnerID=40&md5=8e4794ff52462862570959e801fd7c05
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, H. J.
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, H. J.
author_facet Sadaei, H. J.
author_sort Sadaei, H. J.
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 Inc.
publishDate 2017
url http://eprints.utm.my/id/eprint/76329/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010953682&doi=10.1016%2fj.ijar.2017.01.006&partnerID=40&md5=8e4794ff52462862570959e801fd7c05
_version_ 1643657279946358784
score 13.211869

Similar Items