Prediction of daily peak electricity demand in South Africa using volatility forecasting models

Energy Economics

Volumn 33, Issue 5, 2011, Pages 882-888

  Sigauke, C ^a   Chikobvu, D ^b  

^a UNIVERSITY OF LIMPOPO   (South Africa)

^b UNIVERSITY OF THE FREE STATE   (South Africa)

Author keywords

Daily peak demand; GARCH; Piecewise linear regression; SARIMA; Volatility

Indexed keywords

GARCH; PEAK DEMAND; PIECEWISE LINEAR REGRESSION; SARIMA; VOLATILITY;

ADAPTIVE ALGORITHMS; LINEAR REGRESSION; PIECEWISE LINEAR TECHNIQUES;

FORECASTING;

ACCURACY ASSESSMENT; ALGORITHM; DEMAND ANALYSIS; ELECTRICITY SUPPLY; ERROR ANALYSIS; FORECASTING METHOD; MODEL; PREDICTION; SEASONALITY; VECTOR AUTOREGRESSION;

SOUTH AFRICA;

EID: 80052158112     PISSN: 01409883     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eneco.2011.02.013     Document Type: Article

References (31)

1. Aknouche A., Bentarzi M. On the existence of higher-order moments of periodic GARCH models. Statistics and Probability Letters 2008, 78:3262-3268.
2. Amaral L.F., Souza R.C., Stevenson M. A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting. International Journal of Forecasting 2008, 24:603-615.
3. Amin-Naseri M.R., Soroush A.R. Combined use of unsupervised and supervised learning for daily peak load forecasting. Energy Conversion and Management 2008, 49:1302-1308.
4. Berndt E.K., Hall B.H., Hall R.E., Hausman J.A. Estimation and inference in nonlinear structural models. Annals of Economic and Social Measurement 1974, 4:653-665.
5. Bollerslev T. Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 1986, 31:307-327.
6. Carpinteiro O., Reis A., Silva A. A hierarchical neural model in short-term load forecasting. Applied Soft Computing 2004, 4:405-412.
7. Doornik J.A., Ooms M. Multimodality in GARCH regression models. International Journal of Forecasting 2008, 24:432-448.
8. Engle R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom Inflation. Econometrica 1982, 50:987-1008.
9. Feinberg E., Genethliou D. Load forecasting. Applied Mathematics for Restructured Electric Power Systems: Optimization, Control and Computational Intelligence 2005, 269-285. Springer. J. Chow, F. Wu, J. Momoh (Eds.).
10. Friedman J.H. Multivariate adaptive regression splines. Annals of Statistics 1991, 19(1):1-141.
11. Ghahramani M., Thavaneswaran A. A note on GARCH model identification. Computers and Mathematics with Applications 2008, 55:2469-2475.
12. Goia A., May C., Fusai G., 2010. Functional clustering and linear regression for peak load forecasting. International Journal of Forecasting 26, 700-711.
13. Gosh S. Univariate time-series forecasting of monthly peak demand of electricity in northern India. International Journal of Indian Culture and Business Management 2008, 1:466-474.
14. Hahn H., Meyer-Nieberg S., Pickl S. Electric load forecasting methods: tools for decision making. European Journal of Operational Research 2009, 199:902-907.
15. He, Z., Maheu, J.M., 2010. Real time detection of structural breaks in GARCH models. Computational Statistics and Data Analysis 54(11), 2628-2640.
16. Hekkenberg M., Benders R.M.J., Moll H.C., Schoot Uiterkamp A.J.M. Indications for a changing electricity demand pattern: the temperature dependence of electricity demand in the Netherlands. Energy Policy 2009, 37:1542-1551.
17. Horv́ath L., Kokoszka P., Zitikis R. Distributional analysis of empirical volatility in GARCH processes. Journal of Statistical Planning and Inference 2008, 138:3578-3589.
18. Ismail Z., Yahya A., Mahpol K.A. Forecasting peak load electricity demand using statistics and rule based approach. American Journal of Applied Sciences 2009, 6(8):1618-1625.
19. Kim Y.S., Rachev S.T., Bianchi M.L., Fabozzi F.J., 2010. Tempered stable and tempered infinitely divisible GARCH models. Journal of Banking & Finance 34(9), 2096-2109.
20. Mirasgedis S., Sarafidis Y., Georgopoulou E., Lalasa D.P., Moschovits M., Karagiannis F., Papakonstantinou D. Models for mid-term electricity demand forecasting incorporating weather influences. Energy 2006, 31:208-227.
21. Mulera N., Yohaib V.J. Robust estimates for GARCH models. Journal of Statistical Planning and Inference 2008, 138:2918-2940.
22. Munoz A., Sanchez-Ubeda E.F., Cruz A., Marin J. Short-term forecasting in power sytems: a guided tour. Energy Systems 2010, 2:129-160.
23. Nelson D.B. Conditional heteroskedasticity in asset returns: a new approach. Econometrica 1991, 59:347-370.
24. Nelson D.B., Cao C.Q. Inequality constraints in the univariate GARCH model. Journal of Business & Economic Statistics 1992, 10(2):229-235.
25. Ramanathan R., Engle R., Granger C.W.J., Vahid-Araghi F., Brace C. Short-run forecasts of electricity loads and peaks. International Journal of Forecasting 1997, 13:161-174.
26. Sigauke C., Chikobvu D. Daily peak electricity load forecasting in South Africa using a multivariate non-parametric regression approach. ORiON 2010, 26(2):97-111.
27. Soares L.J., Medeiros M.C. Modelling and forecasting short-term electricity load: a comparison of methods with an application to Brazilian data. International Journal of Forecasting 2008, 24:630-644.
28. Soares L., Souza L. Forecasting electricity demand using generalized long memory. International Journal of Forecasting 2006, 22:17-28.
29. Taylor J.W. Density forecasting for the efficient balancing of the generation and consumption of electricity. International Journal of Forecasting 2006, 22:707-724.
30. Taylor J.W. An evaluation of methods for very short-term load forecasting using minute-by-minute British data. International Journal of Forecasting 2008, 24:645-658.
31. Truong N.-V., Wang L., Wong P.K.C. Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models. Electrical Power and Energy Systems 2008, 30:511-518.