Short Term Load Forecasting Algorithm for Lunar New Year's Day

  • Song, Kyung-Bin (Department of Electrical Engineering, Soongsil University) ;
  • Park, Jeong-Do (Division of Energy & Electrical Engineering, Uiduk University) ;
  • Park, Rae-Jun (Department of Electrical Engineering, Soongsil University)
  • Received : 2017.04.21
  • Accepted : 2017.10.10
  • Published : 2018.03.01


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 Year's Day holidays well known for the highest error occurrence holiday period are analyzed to propose a load forecast technique for Lunar New Year's Day holidays. To solve the insufficient input data problem, the similarity of the load patterns of past Lunar New Year's Day holidays having similar patterns was judged by Euclid distance. Lunar New Year's 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.


Supported by : Korea Institute of Energy Technology Evaluation and Planning (KETEP)


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