A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers

International Journal of Forecasting

Volumn 24, Issue 4, 2008, Pages 659-678

  Brabec, Marek ^a   Konár, Ondřej ^a   Pelikán, Emil ^a   Malý, Marek ^a  

^a INSTITUTE OF COMPUTER SCIENCE   (Czech Republic)

Author keywords

ARIMAX; ARX; Conditional modeling; Generalized linear mixed model; Individual gas consumption; Nonlinear mixed effects model

Indexed keywords

EID: 55349096083     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijforecast.2008.08.005     Document Type: Article

References (34)

1. Amaral L.F., Souza R.C., and Stevenson M. A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting. International Journal of Forecasting 24 (2008) 603-615 (this issue)
2. Anonymous. S-Plus 6 for Windows, Guide for Statisticians (Volume I) (2001), Insightful, Seattle, Washington
3. Bailey J. Load Profiling for Retail Choice: Examining a Complex and Crucial Component of Settlement. The Electricity Journal 13 (2000) 69-74
4. Bartels R., Fiebig D.G., and Nahm D. Regional end-use gas demand in Australia. Economic Record 72 (1996) 319-331
5. Bauwens L., Fiebig D.G., and Steel M.F.J. Estimating end-use demand: A Bayesian approach. Journal of Business and Economic Statistics 12 (1994) 221-231
6. Box G.E.P., and Jenkins G. Time series analysis, forecasting and control (1990), Holden-Day, San Francisco
7. Breslow N.E., and Clayton D.G. Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88 (1993) 9-25
8. Business Forecast Systems, Inc. Forecast Pro (2008). http://www.forecastpro.com Business Forecast Systems, Inc., Belmont, MA. http://www.forecastpro.com. Accessed on 30 April 2008
9. Cancelo J., Espasa A., and Grafe R. Forecasting from one day to one week ahead for the Spanish system operator. International Journal of Forecasting 24 (2008) 588-602 (this issue)
10. Cottet R., and Smith M. Bayesian modeling and forecasting of intraday electricity load. Journal of the American Statistical Association 98 (2003) 839-849
11. Cryer J.D., and Chan K.S. Time series analysis with applications in R (2008), Springer, New York
12. da Silva A.P.A., Ferreira V.H., and Velasquez R.M.G. Input space to neural network based load forecasters. International Journal of Forecasting 24 (2008) 616-629 (this issue)
13. Davidian M., and Giltinan D.M. Nonlinear models for repeated measurement data (1995), Chapman and Hall, London
14. Diebold F.X., and Mariano R.S. Comparing predictive accuracy. Journal of Business and Economic Statistics 13 (1995) 253-263
15. Dordonnat V., Koopman S.J., Ooms M., Dessertaine A., and Collet J. An hourly periodic state space model for modelling French national electricity load. International Journal of Forecasting 24 (2008) 566-587 (this issue)
16. Gerbec D., Gašperič S., Šmon I., and Gubina F. Determining the load profiles of consumers based on fuzzy logic and probability neural networks. IEEE Proceedings - Generation, Transmission and Distribution 151 (2004) 395-400
17. Ghosh J.K., Delampady M., and Samanta T. An introduction to Bayesian analysis (2006), Springer, New York
18. Gil S., and Deferrari J. Generalized model of prediction of natural gas consumption. Transactions of the ASME 126 (2004) 90-97
19. Harvey A., and Koopman S.J. Forecasting hourly electricity demand using time-varying splines. Journal of the American Statistical Association 88 (1993) 1228-1236
20. Little R.J.A., and Rubin D.B. Statistical analysis with missing data (1987), John Wiley & Sons, New York
21. Liu J.M., Chen R., Liu J.M., Liu L.M., and Harris J.L. A semi-parametric time-series approach in modeling hourly electricity loads. Journal of Forecasting 25 (2006) 537-559
22. McCulloch C.E., and Searle S.R. Generalized, linear, and mixed models (2001), John Wiley, New York
23. Pinheiro J.C., and Bates D.M. Mixed-effects models in S and S-plus (2000), Springer, New York
24. R Development Core Team. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria (2008). http://www.r-project.org/ http://www.r-project.org/. Accessed on 30 April 2008
25. Sánchez-Úbeda E.F., and Berzosa A. Modeling and forecasting industrial end-use natural gas consumption. Energy Economics 29 (2007) 710-742
26. Schwarz D., Kubessa M., and Fuhrberg-Baumann J. Load profiles and regression functions for customers not measured according to output in the liberalized gas market. Gas- und Wasserfach 143 (2002) 689-692 (in German)
27. Searle S.R. Linear models (1971), John Wiley, New York
28. Soares L.J., and Medeiros M.C. Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data. International Journal of Forecasting 24 (2008) 630-644 (this issue)
29. Suykens J., Lemmerling P., Favoreel W., De Moor B., Crepel M., and Briol P. Modelling the Belgian gas consumption using neural networks. Neural Processing Letters 4 (1996) 157-166
30. Taylor J.W. An evaluation of methods for very short-term load forecasting using minute-by-minute British data. International Journal of Forecasting 24 (2008) 645-658 (this issue)
31. Venables W.N., and Ripley B.D. Modern applied statistics with S-PLUS (3rd ed.) (1999), Springer, New York
32. Vondráček J., Pelikán E., Konár O., Čermáková J., Eben K., Malý M., et al. A statistical model for the estimation of natural gas consumption. Applied Energy 85 (2008) 362-370
33. Wang G.C.S. Forecasting practices in electric and gas utility companies. Journal of Business Forecasting Methods and Systems 23 (2004) 11-15
34. Wolfinger R., and O'Connell M. Generalized linear mixed models: a pseudo-likelihood approach. Journal of Statistical Computation and Simulation 48 (1993) 233-243