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Short Term Load Forecasting Algorithm for Lunar New Years Day
Kyung-Bin Song, Jeong-Do Park* and Rae-Jun Park**
Abstract Short term load forecasts complexly affected by socioeconomic factors and weather variables have non-linear characteristics. Thus far, researchers have improved load forecast technologies through diverse techniques such as artificial neural networks, fuzzy theories, and statistical methods in order to enhance the accuracy of load forecasts. Short term load forecast errors for special days are relatively much higher than that of weekdays. The errors are mainly caused by the irregularity of social activities and insufficient similar past data required for constructing load forecast models. In this study, the load characteristics of Lunar New Years Day holidays well known for the highest error occurrence holiday period are analyzed to propose a load forecast technique for Lunar New Years Day holidays. To solve the insufficient input data problem, the similarity of the load patterns of past Lunar New Years Day holidays having similar patterns was judged by Euclid distance. Lunar New Years Day holidays periods for 2011-2012 were forecasted by the proposed method which shows that the proposed algorithm yields better results than the comprehensive analysis method or the knowledge-based method.
Keyword Short-term load forecasting, Lunar new years day, Load characteristics, Load patterns
Status Before proofreading
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