Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
| Autor: | Muhammad Hisyam Lee, Cidiney J. Silva, Hossein Javedani Sadaei, Tayyebeh Eslami, Frederico Gadelha Guimares |
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| Rok vydání: | 2017 |
| Předmět: |
Computer science
020209 energy Applied Mathematics Evolutionary algorithm Swarm behaviour Particle swarm optimization 02 engineering and technology Seasonality medicine.disease computer.software_genre Fuzzy logic Theoretical Computer Science Term (time) Autoregressive model Artificial Intelligence Moving average 0202 electrical engineering electronic engineering information engineering medicine Econometrics 020201 artificial intelligence & image processing Data mining computer Software |
| Zdroj: | International Journal of Approximate Reasoning. 83:196-217 |
| ISSN: | 0888-613X |
| DOI: | 10.1016/j.ijar.2017.01.006 |
| Popis: | 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. To increase accuracy of forecasts inside seasonal long memory time series, a hybrid method is proposed.The proposed method is based on a combination of Fuzzy Time Series and SARFIMA.High-order Fuzzy Time Series is adopted to be revised for developing the proposed method.Particle Swarm Optimization is applied for parameters estimation.Many long memory seasonal datasets, including short term load data are employed for evaluation purpose. |
| Databáze: | OpenAIRE |
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