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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(1): 57-61 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.1.572

A Fuzzy Logic Based Short Term Load Forecast for the Holidays

Hasan H. Çevik and Mehmet Çunkaş
Abstract—Electric load forecasting is important for economic operation and planning. Holiday load consumptions are very different than normal days and does not follow the regular trend of normal days. Also data for the holidays less than other normal days. So making an accuracy holiday load forecast model is a difficult task. The purpose of this paper is presents different models using fuzzy logic method without weather information. Firstly holidays are classified according to their characteristics and historical load shapes. Each fuzzy model have three inputs and one output. While historical data from past years, consumption data from last week and the type of holiday (national, religious) are selected as inputs, the output is hourly forecasted holiday load. The data between the years 2009 and 2011 are used to design the forecasting models. The model performances are evaluated with the real data of the year 2012. The results of models are compared each other and show that proposed Model 2(scaled model) is more successful than Model 1. This paper shows that fuzzy logic can give good results for the holiday short term load forecast.

Index Terms—Fuzzy logic, holiday load forecasting, short-term load forecasting.

The authors are with the Selçuk University, Technology Faculty, Department of Electrical & Electronics Engineering, Konya, Turkey (e-mail: hasanhcevik@selcuk.edu.tr, mcunkas@selcuk.edu.tr).

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Cite: Hasan H. Çevik and Mehmet Çunkaş, "A Fuzzy Logic Based Short Term Load Forecast for the Holidays," International Journal of Machine Learning and Computing vol.6, no. 1, pp. 57-61, 2016.

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