A wavelet based hybrid SARIMA-ETS model to forecast electricity consumption


Abstract


Energy plays a fundamental role in the process of economic growth of a nation. Growth in the demand and consumption of energy is linked to economic output of a country as measured by gross domestic product. In view of formulating sustainable strategies in the energy service market, modelling and forecasting future demand of electricity is an integral part of decision support system of energy production in developed and developing world. Recent years have witnessed an increasing interest in providing prediction models of electrical energy consumption with greater accuracy. Besides the use of logistic and Harvey logistic growth curve models, the stochastic forecasting has been carried out using Box-Jenkins ARIMA, Holt-Winter, SARIMA and time series ANN models etc. However, their performance is far from perfect and it is especially true when the data contain complex nonlinear pattern and volatility. In this article, we propose a hybrid model that splits a time series into an approximate and a detailed component via discrete wavelet transform and then SARIMA and ETS models are used to fit and forecast the wavelet approximation and the detailed component respectively. Using the real monthly electricity consumption data from eight north eastern states of India during 2004-2015, we have developed the proposed model. Results of our investigation successfully demonstrates the higher degree of prediction accuracy of the proposed model than a data driven Box-Jenkins ARIMA model in terms of various performance accuracy measurement statistics and produces a substantial reduction in forecast errors.

Keywords: Discrete wavelet transform; Box-Jenkins ARIMA; SARIMA; ETS; Electricity consumption forecasting

References


Afanasyev, D. O. and Fedorova, E. A. (2016). The long-term trends on the electricity markets: Comparison of empirical mode and wavelet decompositions. Energy Economics, 56:432-442.

Akaike, H. (1974). A new look at the statistical model identication. IEEE transactions on automatic control, 19(6):716-723.

Bates, J. M. and Granger, C. W. (1969). The combination of forecasts. Journal of the Operational Research Society, 20(4):451-468.

Box, G. E. P. and Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day San Francisco, California.

Box, G. E. P. and Jenkins, G. M. (1990). Time Series Analysis: Forecasting and Control. Holden-Day Incorporated.

Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control. Prentice Hall Press, Englewood Clis, NJ, third edition.

Brockwell, P. J. and Davis, R. A. (2006). Introduction to time series and forecasting. Springer Science and Business Media.

Brown, R. G. (1959). Statistical forecasting for inventory control.

Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multi-model Inference: A Practical Information-Theoretic Approach. Springer-Verlag, second edition.

Catal~ao, J. P. d. S., Pousinho, H. M. I., and Mendes, V. M. F. (2011). Hybrid waveletpsoans approach for short-term electricity prices forecasting. IEEE Transactions on Power Systems, 26(1):137-144.

Cavanaugh, J. E. (1997). Unifying the derivations for the akaike and corrected akaike information criteria. Statistics & Probability Letters, 33(2):201-208.

dos Santos Coelho, L. and Santos, A. A. (2011). A rbf neural network model with garch errors: application to electricity price forecasting. Electric Power Systems Research, 81(1):74-83.

Ediger, V. S. and Akar, S. (2007). Arima forecasting of primary energy demand by fuel in turkey. Energy Policy, 35(3):1701-1708.

Egelioglu, F., Mohamad, A., and Guven, H. (2001). Economic variables and electricity consumption in northern cyprus. Energy, 26(4):355-362.

Erdogdu, E. (2007). Electricity demand analysis using cointegration and arima modelling: A case study of turkey. Energy policy, 35(2):1129-1146.

Garcia, R. C., Contreras, J., Van Akkeren, M., and Garcia, J. B. C. (2005). A garch forecasting model to predict day-ahead electricity prices. IEEE transactions on power systems, 20(2):867-874.

Gencay, R., Selcuk, F., and Whitcher, B. J. (2001). An introduction to wavelets and other ltering methods in nance and economics. Academic press.

Granger, C. W. J. and Newbold, P. (2014). Forecasting economic time series. Academic Press.

Haar, A. (1910). Zur theorie der orthogonalen funktionensysteme. Mathematische Annalen, 69(3):331-371.

Holt, C. C. (1957). Forecasting trends and seasonals by exponentially weighted averages.

Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D. (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.

Hyndman, R. J., Athanasopoulos, G., Razbash, S., Schmidt, D., Zhou, Z., Khan, Y., Bergmeir, C., and Wang, E. (2014). forecast: Forecasting functions for time series and linear models. R package version 5.5.

Hyndman, R. J. and Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4):679-688.

Hyndman, R. J., Koehler, A. B., Snyder, R. D., and Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3):439-454.

Liu, H. and Shi, J. (2013). Applying arma-garch approaches to forecasting short-term electricity prices. Energy Economics, 37:152-166.

Meitz, M. and Saikkonen, P. (2013). Maximum likelihood estimation of a noninvertible arma model with autoregressive conditional heteroskedasticity. Journal of Multivariate Analysis, 114:227-255.

Mohamed, Z. and Bodger, P. (2003). Analysis of the logistic model for predicting new zealand electricity consumption.

Mohamed, Z. and Bodger, P. (2005). A comparison of logistic and harvey models for electricity consumption in new zealand. Technological Forecasting and Social Change, 72(8):1030-1043.

North-Eastern Regional Power Committee (NERPC) (2015). Annual Reports, 2005-06; 2007-08; 2009-10; 2011-12; 2012-13 and 2014-15. Ministry of Power, Government of India, Shillong-793006, Meghalaya.

R Development Core Team (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-90005107-0.

Schwarz, G. et al. (1978). Estimating the dimension of a model. The annals of statistics, 6(2):461-464.

Shrivastava, N. A. and Panigrahi, B. K. (2014). A hybrid wavelet-elm based short term price forecasting for electricity markets. International Journal of Electrical Power & Energy Systems, 55:41-50.

Sims, C. A. et al. (1986). Are forecasting models usable for policy analysis? Federal Reserve Bank of Minneapolis Quarterly Review, 10(1):2-16.

Tan, Z., Zhang, J., Wang, J., and Xu, J. (2010). Day-ahead electricity price forecasting using wavelet transform combined with arima and garch models. Applied Energy, 87(11):3606-3610.

Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8):799-805.

Walker James, S. (1999). A primer on wavelets and their scientic applications [livro].[sl].

Wang, J., Liu, F., Song, Y., and Zhao, J. (2016). A novel model: Dynamic choice articial neural network (dcann) for an electricity price forecasting system. Applied Soft Computing, 48:281-297.

Wang, Y., Wang, J., Zhao, G., and Dong, Y. (2012). Application of residual modication approach in seasonal arima for electricity demand forecasting: a case study of china. Energy Policy, 48:284-294.

Wheelwright, S., Makridakis, S., and Hyndman, R. J. (1998). Forecasting: methods and applications. John Wiley & Sons.

Whitcher, B. (2015). Waveslim: Basic wavelet routines for one-, two- and threedimensional signal processing. R package version 1.7.5.

Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3):324-342.

Yan, X. and Chowdhury, N. A. (2013). Mid-term electricity market clearing price forecasting: A hybrid lssvm and armax approach. International Journal of Electrical Power & Energy Systems, 53:20-26.

Yan, Y. Y. (1998). Climate and residential electricity consumption in hong kong. Energy, 23(1):17-20.

Zahan, M. and Kenett, R. S. (2013). Modeling and forecasting energy consumption in the manufacturing industry in south asia. International Journal of Energy Economics and Policy, 3(1):87.

Zhang, J. and Tan, Z. (2013). Day-ahead electricity price forecasting using wt, clssvm and egarch model. International Journal of Electrical Power & Energy Systems, 45(1):362-368.


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