A study on electricity demand forecasting based on time series clustering in smart grid

Title & Authors
A study on electricity demand forecasting based on time series clustering in smart grid
Sohn, Hueng-Goo; Jung, Sang-Wook; Kim, Sahm;

Abstract
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).
Keywords
time series clustering;TBATS model;double seasonal Holt-Winters model;electricity demand forecasting;dynamic time warping;
Language
Korean
Cited by
References
1.
Bakhat, M. and Rossello, J. (2011). Estimation of tourism-induced electricity consumption: the case study of Balearics Islands, Spain, Energy Economics, 33, 437-444.

2.
Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series, In KDD Workshop, 10, 359-370.

3.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1976). Linear nonstationary models, Time Series Analysis, Fourth Edition, 93-136.

4.
Caiado, J., Crato, N., and Pena, D. (2006). A periodogram-based metric for time series classification, Computational Statistics & Data Analysis, 50, 2668-2684.

5.
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.

6.
Erdogdu, E. (2010). Natural gas demand in Turkey, Applied Energy, 87, 211-219.

7.
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.

8.
Hong, W. C. (2009). Electric load forecasting by support vector model, Applied Mathematical Modelling, 33, 2444-2454.

9.
Hosking, J. R. (1981). Fractional differencing, Biometrika, 68, 165-176.

10.
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.

11.
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.

12.
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.

13.
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.

14.
Lee, H. N., Han, J. H., and Lee, M. H. (2010). Electricity peak equation: estimation and prediction, Korean Energy Economic Review, 9, 83-99.

15.
Lee, H. R. and Shin, H. J. (2011). Electricity demand forecasting based on support vector regression, IE Interfaces, 24, 351-361.

16.
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.

17.
Lise, W. and Montfort, K. V. (2007). Energy consumption and GDP in Turkey: is there a cointegration relationship, Energy Economics, 29, 1166-1178.

18.
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.

19.
Montero, P. and Vilar, J. A. (2014). TSclust: An R package for time series clustering, Journal of Statistical Software, 62, 1-43.

20.
Pao, H. T. (2009). Forecasting energy consumption in Taiwan using hybrid nonlinear models, Energy, 34, 1438-1446.

21.
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.

22.
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.

23.
Sari, R. and Soytas, U. (2004). Disaggregate energy consumption, employment and income in Turkey, Energy Economics, 26, 335-344.

24.
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.

25.
Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing, Journal of the Operational Research Society, 54, 799-805.

26.
Taylor, J. W. (2010). Triple seasonal methods for short-term load forecasting, European Journal of Operational Research, 204, 139-152.

27.
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.

28.
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.

29.
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.