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A study on electricity demand forecasting based on time series clustering in smart grid
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 Title & Authors
A study on electricity demand forecasting based on time series clustering in smart grid
Sohn, Hueng-Goo; Jung, Sang-Wook; Kim, Sahm;
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This paper forecasts electricity demand as a critical element of a demand management system in Smart Grid environment. We present a prediction method of using a combination of predictive values by time series clustering. Periodogram-based normalized clustering, predictive analysis clustering and dynamic time warping (DTW) clustering are proposed for time series clustering methods. Double Seasonal Holt-Winters (DSHW), Trigonometric, Box-Cox transform, ARMA errors, Trend and Seasonal components (TBATS), Fractional ARIMA (FARIMA) are used for demand forecasting based on clustering. Results show that the time series clustering method provides a better performances than the method using total amount of electricity demand in terms of the Mean Absolute Percentage Error (MAPE).
time series clustering;TBATS model;double seasonal Holt-Winters model;electricity demand forecasting;dynamic time warping;
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