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Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid

Author

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  • Heung-gu Son

    (Department of Short-term Demand Forecasting, Korea Power Exchange, Naju 58322, Korea)

  • Yunsun Kim

    (Department of Applied Statistics, Chung-ang University, Seoul 06974, Korea)

  • Sahm Kim

    (Department of Applied Statistics, Chung-ang University, Seoul 06974, Korea)

Abstract

This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box–Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt–Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), and neural network nonlinear autoregressive (NN-AR) are used for demand forecasting based on clustering. The results show that the time-series clustering method performs better than the method using the total amount of electricity demand in terms of the mean absolute percentage error (MAPE).

Suggested Citation

  • Heung-gu Son & Yunsun Kim & Sahm Kim, 2020. "Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid," Energies, MDPI, vol. 13(9), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2377-:d:355978
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    References listed on IDEAS

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    Cited by:

    1. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.

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