Serviços Personalizados
Artigo
Indicadores
Links relacionados
- Citado por Google
- Similares em Google
Compartilhar
Journal of Energy in Southern Africa
versão On-line ISSN 2413-3051
versão impressa ISSN 1021-447X
J. energy South. Afr. vol.23 no.3 Cape Town 2012
Regression-SARIMA modelling of daily peak electricity demand in South Africa
Delson ChikobvuI; Caston SigaukeII
IDepartment of Mathematical Statistics and Actuarial Science, University of the Free State, South Africa
IIDepartment of Statistics and Operations Research, School of Mathematical and Computer Sciences, University of Limpopo, South Africa
ABSTRACT
In this paper, seasonal autoregressive integrated moving average (SARIMA) and regression with SARIMA errors (regression-SARIMA) models are developed to predict daily peak electricity demand in South Africa using data for the period 1996 to 2009. The performance of the developed models is evaluated by comparing them with Winter's triple exponential smoothing model. Empirical results from the study show that the SARIMA model produces more accurate short-term forecasts. The regression-SARIMA modelling framework captures important drivers of electricity demand. These results are important to decision makers, load forecasters and systems operators in load flow analysis and scheduling of electricity.
Keywords: daily peak demand, SARIMA, regression-SARIMA, short term load forecasting
Full text available only in PDF format.
References
Amaral, L.F., Souza, R.C. & Stevenson, M. (2008). A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting. International Journal of Forecasting 24(4), 603-615. [ Links ]
Amin-Naseri, M.R. & Soroush, A.R. (2008). Combined use of unsupervised and supervised learning for daily peak load forecasting. Energy Conversion and Management 49(6), 1302-1308. [ Links ]
Amusa, H., Amusa, K. & Mabugu, R. (2009). Aggregate demand for electricity in South Africa: An analysis using the bounds testing approach to co integration. Energy Policy 37(10), 4167- 4175. [ Links ]
Azadeh, A., Ghaderi, S.F, & Sohrabkhani S. (2007). Forecasting electrical consumption by integration on neural network, time series and ANOVA. Applied Mathematics and Computation 186(2), 1753-1761. [ Links ]
Carpinteiro, O., Reis, A. & Silva, A. (2004). A hierarchical neural model in short-term load Forecasting. Applied Soft Computing 4(4), 405-412. [ Links ]
Feinberg, E. & Genethliou, D. (2005). Load Forecasting. In J. Chow, FF Wu and J. Momoh (Eds.). Applied Mathematics for Restructured Electric Power Systems: Optimization, Control and Computational Intelligence. (pp. 269-285). Springer. [ Links ]
Ghosh, S. (2008). Univariate time-series forecasting of monthly peak demand of electricity in northern India. International Journal of Indian Culture and Business Management 1, 466-474. [ Links ]
Goia, A., May, C. & Fusai, G. (2010). Functional clustering and linear regression for peak load forecasting. International Journal of Forecasting 26(4), 700-711. [ Links ]
Granger, C.W.J. & Jeon, Y (2007). Long-term forecasting and evaluation. International Journal of Forecasting 23(4), 539-551. [ Links ]
Hahn, H., Meyer-Nieberg, S. & Pickl, S. (2009). Electric load forecasting methods: Tools for decision making. European Journal of Operational Research 199(3), 902-907. [ Links ]
Harvey, A. & Koopman, S.J. (1993). Forecasting Hourly Electricity Demand using Time-Varying Splines. Journal of the American Statistical Association 88(424), 1228-1236. [ Links ]
Ismail, Z., Jamaluddin, FF & Jamaluddin, F (2008). Time series regression model for forecasting Malaysian electricity load demand. Asian Journal of Mathematics and Statistics. 1(3), 139-149. [ Links ]
Ismail, Z., Yahya, A. & Mahpol, K.A. (2009). Forecasting peak load electricity demand using statistics and rule based approach. American Journal of Applied Sciences 6 (8), 1618-625. [ Links ]
Munoz A, Sanchez-Ubeda EF, Cruz A and Marin J. 2010. Short-term forecasting in power systems: a guided tour. Energy Systems, 2, 129-160. [ Links ]
Ramanathan, R., Engle, R., Granger, C.W.J, Vahid-Araghi FF & Brace, C. (1997). Short-run forecasts of electricity loads and peaks. International Journal of Forecasting 13(2), 161-174. [ Links ]
Sigauke, C., & Chikobvu, D. (2010). Daily peak electricity load forecasting in South Africa using a multivariate non-parametric regression approach. ORiON 26 (2), 97-111. [ Links ]
Sigauke, C., & Chikobvu, D. (2011). Prediction of daily peak electricity demand in South Africa using volatility forecasting models. Energy Economics 33, 882-888. [ Links ]
Soares, L. & Souza, L. (2006). Forecasting electricity demand using generalized long memory. International Journal of Forecasting 22(1), 17-28. [ Links ]
Soares, L.J. & Medeiros, M.C. (2008). Modelling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data. International Journal of Forecasting 24(4), 630-644. [ Links ]
Suganthi, L. & Samuel A.A. (2012). Energy models for demand forecasting - A review. Renewable and Sustainable Energy Reviews 16, 1223- 1240. [ Links ]
Sumer, K.K., Goktas, O. & Hepsag, A. (2009). The application of seasonal latent variable in forecasting electricity demand as an alternative method. Energy Policy 37(4), 1317-1322. [ Links ]
Taylor, J., de Menezes, L. & Mcsharry, P (2006). A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Forecasting 22, 1-16. [ Links ]
Taylor, J.W. (2008) An evaluation of methods for very short-term load forecasting using minute-by-minute British data. International Journal of Forecasting 24, 645-658. [ Links ]
Truong, N-V., Wang, L. & Wong, P.K.C. (2008). Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models. International Journal of Electrical Power & Energy Systems 30(9), 511-518. [ Links ]
Wang, J., Zhu, W. & Sun, D. (2009). A trend fixed on firstly and seasonal adjustment model combined with the - SVR for short-term forecasting of electricity demand. Energy Policy 37(11), 4901-4909. [ Links ]
Received 7 June 2011
Revised 14 May 2012