Electricity Load Forecasting using Hybrid of Multiplicative Double Seasonal Exponential Smoothing Model with Artificial Neural Network
Keywords:Electricity load forecasting, multiplicative double seasonal exponential smoothing, ANN, hybrid model
AbstractElectricity load forecasting often has many properties such as the nonlinearity, double seasonal cycles, and others those may be obstacles for the accuracy of forecasting using some classical statistical models. Many papers in this field have proposed using double seasonal (DS) exponential smoothing model to forecast. These papers indicated that electricity load forecasting using DS exponential smoothing model has better fit. Using artificial neural network (ANN) as a modern approach may be used for superior fitted forecasting, since this approach can deal with the non-linearity components of load data. The purpose of this paper is to improve the electricity load forecasting by building the hybrid model that includes a double seasonal exponential smoothing with an artificial neural network. This hybrid model will study the double seasonal effects and non-linearity components together based on the electricity load data. The strategy of building this hybrid model is by entering ANN output as an input in double seasonal exponential smoothing model. The data sets are taken from three stations with different electricity load characteristics such as a residential, industrial and city center. The electricity load testing forecast of DS exponential smoothing-ANN hybrid model gave the most minimum mean absolute percentage error (MAPE) measurement comparing with the electricity load testing forecasts of DS exponential smoothing and ANN for all electricity load data sets. In conclusion, DS exponential smoothing-ANN hybrid model are the most fitted for every electricity load data which contains the double seasonal effects and non-linearity components.
Xia, C., J. b. Wang and K. McMenemy. 2010. Short, Medium and Long Term Load Forecasting Model and Virtual Load Forecaster Based on Radial Basis Function Neural Networks. Electrical Power and Energy Systems. 32: 743â€“750.
Taylor, J. W. 2003. Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing. Journal of Operational Research Society. 54: 799â€“805.
Taylor, J. W. and P. E. McSharry. 2007. Short-Term Load Forecasting Methods An Evaluation Based on European Data. IEEE Transactions on Power Systems. 22(4): 2213â€“2219.
Taylor, J. W. 2010. Triple Seasonal Methods for Short-Term Electricity Demand Forecasting. European Journal of Operational Research. 204: 139â€“152.
Souza, R. C., M. Barros and C. V. C. Miranda. 2007. Short Term Load Forecasting Using Double Seasonal Exponential Smoothing and Interventions To Account for Holidays and Temperature Effects. TLAIO II-2 do Taller Latino Iberoamericano de InvestigaciÃ³n de Operaciones. Acapulco, MÃ©xico. 1â€“8.
Beak, M. 2008. A Study on Forecasting High Frequency Time Series With Multiple Seasonal Patterns. USA: University of Massachusetts Amherst.
Gould, P. G., A. B. Koehler, J. K. Ord, R. D. Snyder, R. J. Hyndman and F. V. Araghi. 2008. Forecasting Time Series with Multiple Seasonal Patterns. European Journal of Operational Research. 191: 207â€“222.
Faya, D., J. V. Ringwoodb, M. Condona and M. Kelly. 2003. 24-Helectrical Load Data: a Sequential or Partitioned Time Series. Neurocomputing. 55: 469â€“498.
Tee, C. Y., J. B. Cardell and G. W. Ellis. 2009. Short-Term Load Forecasting Using Artificial Neural Networks. Conferene Name. 4â€“6 October. Starkville, MS, USA: IEEE. 1â€“6.
Wang, Y., D. Niu and L. Ji. 2012. Short-Term Power Load Forecasting Based on IVL-BP Neural Network Technology. Systems Engineering Procedia. 4: 168â€“174.
Moturi, C. A. and F. K. Kioko. 2013. Use of Artificial Neural Networks for Short-Term Electricity Load Forecasting of Kenya National Grid Power System. International Journal of Computer Applications. 63(2): 25â€“30.
Taylor, J. W., L. M. d. Menezes and P. E. McSharry. 2006. A Comparison of Univariate Methods for Forecasting Electricity Demand Up to a Day Ahead. International Journal of Forecasting. 22: 1â€“16.
Yu, L. 2007. Foreign-Exchange-Rate Forecasting With Artificial Neural Networks. International Series in Operations Research & Management Science. 107: 121â€“131.
Samreen, F. 2007. Hybrid System of Simple Exponential Smoothing and Neural Network for KSE100 Index. Market Forces-Journal of Management Thought. 2(4).
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