Comparison of Short-Term Load Forecasting Based on Kalimantan Data
DOI:
https://doi.org/10.29244/ijsa.v5i2p243-259Keywords:
forecasting, time series, triple SARIMAAbstract
This paper investigates a case study on short term forecasting for East Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity recorded at hourly intervals contains more than one seasonal pattern. There is a great attraction in using a modelling time series method that is able to capture triple seasonalities. The Triple SARIMA model has been adapted for this purpose and competitive for modelling load. Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions and comparing model criteria, we propose and demonstration the triple Seasonal Autoregressive Integrated Moving Average model with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of electricity load Kalimantan data for planning, operation maintenance and market related activities.
Downloads
References
Arora, S., & Taylor, J. W. (2013). Short-term forecasting of anomalous load using rule-based triple seasonal methods. IEEE Transactions on Power Systems, 28(3): 3235–3242.
Dordonnat, V., Koopman, S. J., Ooms, M., Dessertaine, A., & Collet, J. (2008). An hourly periodic state space model for modelling French national electricity load. International Journal of Forecasting, 24(4): 566–587.
Fidalgo, J., & Lopes, J. P. (2005). Load forecasting performance enhancement when facing anomalous events. IEEE Transactions on Power Systems, 20(1): 408–415.
Hyde, O., & Hodnett, P. (1993). Rule-based procedures in short-term electricity load forecasting. IMA Journal of Management Mathematics, 5(1): 131–141.
Hyde, O., & Hodnett, P. (1997). An adaptable automated procedure for short-term electricity load forecasting. IEEE Transactions on Power Systems, 12(1): 84–94.
Kim, K.-H., Youn, H.-S., & Kang, Y.-C. (2000). Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method. IEEE Transactions on Power Systems, 15(2): 559–565.
Lamedica, R., Prudenzi, A., Sforna, M., Caciotta, M., & Cencellli, V. O. (1996). A neural network based technique for short-term forecasting of anomalous load periods. IEEE Transactions on Power Systems, 11(4): 1749–1756.
Rahman, S., & Bhatnagar, R. (1988). An expert system based algorithm for short term load forecast. IEEE Transactions on Power Systems, 3(2): 392–399.
Soares, L. J., & Medeiros, M. C. (2008). Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data. International Journal of Forecasting, 24(4): 630–644.
Song, K.-B., Baek, Y.-S., Hong, D. H., & Jang, G. (2005). Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Transactions on Power Systems, 20(1): 96–101.
Srinivasan, D., Chang, C., & Liew, A. (1995). Demand forecasting using fuzzy neural computation, with special emphasis on weekend and public holiday forecasting. IEEE Transactions on Power Systems, 10(4): 1897–1903.
