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Short-term Electric Load Forecasting Using Data Mining Technique
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 Title & Authors
Short-term Electric Load Forecasting Using Data Mining Technique
Kim, Cheol-Hong; Koo, Bon-Gil; Park, June-Ho;
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 Abstract
In this paper, we introduce data mining techniques for short-term load forecasting (STLF). First, we use the K-mean algorithm to classify historical load data by season into four patterns. Second, we use the k-NN algorithm to divide the classified data into four patterns for Mondays, other weekdays, Saturdays, and Sundays. The classified data are used to develop a time series forecasting model. We then forecast the hourly load on weekdays and weekends, excluding special holidays. The historical load data are used as inputs for load forecasting. We compare our results with the KEPCO hourly record for 2008 and conclude that our approach is effective.
 Keywords
Data mining;K-mean algorithm;k-NN algorithm;Short-term load forecasting;Time series forecasting;
 Language
English
 Cited by
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A data mining based load forecasting strategy for smart electrical grids, Advanced Engineering Informatics, 2016, 30, 3, 422  crossref(new windwow)
3.
Application of hybrid computational intelligence models in short-term bus load forecasting, Expert Systems with Applications, 2016, 54, 105  crossref(new windwow)
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Clustering based day-ahead and hour-ahead bus load forecasting models, International Journal of Electrical Power & Energy Systems, 2016, 80, 171  crossref(new windwow)
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