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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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Bakhat, M. and Rossello, J. (2011). Estimation of tourism-induced electricity consumption: the case study of Balearics Islands, Spain, Energy Economics, 33, 437-444. crossref(new window)

Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series, In KDD Workshop, 10, 359-370.

Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1976). Linear nonstationary models, Time Series Analysis, Fourth Edition, 93-136.

Caiado, J., Crato, N., and Pena, D. (2006). A periodogram-based metric for time series classification, Computational Statistics & Data Analysis, 50, 2668-2684. crossref(new window)

De Livera, A. M., Hyndman, R. J., and Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106, 1513-1527. crossref(new window)

Erdogdu, E. (2010). Natural gas demand in Turkey, Applied Energy, 87, 211-219. crossref(new window)

Hong, H. W., Park, M. J., and Cho, S. S. (2009). Comparison study of time series clustering methods, The Korean Journal of Applied Statistics, 22, 1203-1214. crossref(new window)

Hong, W. C. (2009). Electric load forecasting by support vector model, Applied Mathematical Modelling, 33, 2444-2454. crossref(new window)

Hosking, J. R. (1981). Fractional differencing, Biometrika, 68, 165-176. crossref(new window)

Hwang, H. M., Lee, S. H., Park, J. B., Park, Y. K., and Son, S. Y. (2015). Load forecasting using hierarchical clustering method for building, The Transaction of the Korean Institute of Electrical Engineers, 64, 41-47.

Jang, S. W., Park, Y. J., and Kim, G. Y. (2010). Time series pattern recognition based on branch and bound dynamic time warping, Journal of KISS: Software and Applications, 37, 584-589.

Jung, S. W. and Kim, S. (2014). Electricity demand forecasting for daily peak load with seasonality and temperature effects, The Korean Journal of Applied Statistics, 27, 843-853. crossref(new window)

Kim, W. S. and Jeon, B. K. (2010). Overview of long-term electricity demand forecasting mechanism for national long-term electricity resource planning, Transaction of Korean Institute of Electrical Engineers, 59, 1581-1586.

Lee, H. N., Han, J. H., and Lee, M. H. (2010). Electricity peak equation: estimation and prediction, Korean Energy Economic Review, 9, 83-99.

Lee, H. R. and Shin, H. J. (2011). Electricity demand forecasting based on support vector regression, IE Interfaces, 24, 351-361. crossref(new window)

Lee, J. S., Shon, H. G., and Kim, S. (2013). Daily peak load forecasting for electricity demand by time series models, The Korean Journal of Applied Statistics, 26, 349-360. crossref(new window)

Lise, W. and Montfort, K. V. (2007). Energy consumption and GDP in Turkey: is there a cointegration relationship, Energy Economics, 29, 1166-1178. crossref(new window)

Liu, J., Shu, Y., Zhang, L. and Xue, F. (1999). Traffic modeling based on FARIMA models, IEEE Canadian Conference on Electrical and Computer Engineering, 621-624.

Montero, P. and Vilar, J. A. (2014). TSclust: An R package for time series clustering, Journal of Statistical Software, 62, 1-43.

Pao, H. T. (2009). Forecasting energy consumption in Taiwan using hybrid nonlinear models, Energy, 34, 1438-1446. crossref(new window)

Park, S. S., Son, H. S., Lee, D. G., Ji, E. M., Kim, H.-S., and Ryu, K. H. (2009). Short-term power load forecasting using time pattern for u-city application, Journal of Korea Spatial Information System Society, 11, 177-181.

Park, W. G. and Kim, S. (2012). The performance of time series models to forecast short-term electricity demand, Communications for Statistical Applications and Methods, 19, 869-876. crossref(new window)

Sari, R. and Soytas, U. (2004). Disaggregate energy consumption, employment and income in Turkey, Energy Economics, 26, 335-344. crossref(new window)

Sozen, A. and Arcaklioglu, E. (2007). Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey, Energy Policy, 35, 4981-4992. crossref(new window)

Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing, Journal of the Operational Research Society, 54, 799-805. crossref(new window)

Taylor, J. W. (2010). Triple seasonal methods for short-term load forecasting, European Journal of Operational Research, 204, 139-152. crossref(new window)

Taylor, J. W., De Menezes, L. M., and McSharry, P. E. (2006). A comparison of univariate methods for forecasting electricity demand up to a day ahead, International Journal of Forecasting, 22, 1-16. crossref(new window)

Vilar, J. A., Alonso, A. M., and Vilar, J. M. (2010). Non-linear time series clustering based on non-parametric forecast densities, Computational Statistics & Data Analysis, 54, 2850-2865. crossref(new window)

Wang, J., Zhu, W., Zhang, W., and Sun, D. (2009). A trend fixed on firstly and seasonal adjustment model combined with the ${\epsilon}$-SVR for short-term forecasting of electricity demand, Energy Policy, 37, 4901-4909. crossref(new window)