Advanced fuzzy time series applied to short term load forecasting

This paper proposes the application of advanced fuzzy time series in order to provide short-term load forecasting, which consists of predicting future demands from up to a week. An accurate forecast can influence significantly the availability and reliability of electrical systems. In this work, we tested different fuzzy time series algorithms in order to provide hourly, daily and weekly forecast of the demand in the Polish Electric System. The presented methods have achieved some interesting results to the problem.

[1]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[2]  Shyi-Ming Chen,et al.  Forecasting enrollments based on fuzzy time series , 1996, Fuzzy Sets Syst..

[3]  Frederico G. Guimarães,et al.  Interval forecasting with Fuzzy Time Series , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[4]  Frederico G. Guimarães,et al.  Stock market forecasting by using a hybrid model of exponential fuzzy time series , 2016, Int. J. Approx. Reason..

[5]  Pritpal Singh,et al.  A brief review of modeling approaches based on fuzzy time series , 2015, International Journal of Machine Learning and Cybernetics.

[6]  Zhongyi Hu,et al.  Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms , 2013, TheScientificWorldJournal.

[7]  Kunhuang Huarng,et al.  Effective lengths of intervals to improve forecasting in fuzzy time series , 2001, Fuzzy Sets Syst..

[8]  Ajay Shekhar Pandey,et al.  Short-term load forecasting methods: A review , 2016, 2016 International Conference on Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES).

[9]  Abbas Khosravi,et al.  A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .

[10]  B. Chissom,et al.  Fuzzy time series and its models , 1993 .

[11]  Grzegorz Dudek,et al.  Neural networks for pattern-based short-term load forecasting: A comparative study , 2016, Neurocomputing.

[12]  G. Gross,et al.  Short-term load forecasting , 1987, Proceedings of the IEEE.

[13]  Cláudia Antunes,et al.  Temporal Data Mining: an overview , 2001 .

[14]  Shyi-Ming Chen,et al.  FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES , 2002, Cybern. Syst..

[15]  Song Li,et al.  An ensemble approach for short-term load forecasting by extreme learning machine , 2016 .

[16]  Hui-Kuang Yu.  Weighted fuzzy time series models for TAIEX forecasting , 2005 .

[17]  Grzegorz Dudek.  Pattern-based local linear regression models for short-term load forecasting , 2016 .

[18]  Grzegorz Dudek,et al.  Pattern similarity-based methods for short-term load forecasting - Part 2: Models , 2015, Appl. Soft Comput..

[19]  Grzegorz Dudek,et al.  Pattern similarity-based methods for short-term load forecasting - Part 1: Principles , 2015, Appl. Soft Comput..

[20]  Mustafa Mat Deris,et al.  Improved Weight Fuzzy Time Series as used in the Exchange rates Forecasting of US Dollar to Ringgit Malaysia , 2013, Int. J. Comput. Intell. Appl..