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

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

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Official URL: http://dx.doi.org/10.1016/j.ijar.2017.01.006


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.

Item Type:Article
Additional Information:RADIS System Ref No:PB/2017/12066
Uncontrolled Keywords:fuzzy time series, particle swarm optimization
Subjects:Q Science
ID Code:66169
Deposited By: Widya Wahid
Deposited On:13 Jul 2017 15:18
Last Modified:13 Jul 2017 15:18

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