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

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  • Brabec, Marek
  • Konár, Ondrej
  • Pelikán, Emil
  • Malý, Marek
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    Abstract

    This study deals with the description and prediction of the daily consumption of natural gas at the level of individual customers. Unlike traditional group averaging approaches, we are faced with the irregularities of individual consumption series posed by inter-individual heterogeneity, including zeros, missing data, and abrupt consumption pattern changes. Our model is of the nonlinear regression type, with individual customer-specific parameters that, nevertheless, have a common distribution corresponding to the nonlinear mixed effects model framework. It is advantageous to build the model conditionally. The first condition, whether a particular customer has consumed or not, is modeled as a consumption status in an individual fashion. The prediction performance of the proposed model is demonstrated using a real dataset of 62 individual customers, and compared with two more traditional approaches: ARIMAX and ARX.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 24 (2008)
    Issue (Month): 4 ()
    Pages: 659-678

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    Handle: RePEc:eee:intfor:v:24:y:2008:i:4:p:659-678

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

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

    References

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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
    2. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    3. Soares, Lacir J. & Medeiros, Marcelo C., 2008. "Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 630-644.
    4. Bailey, Jeffrey, 2000. "Load Profiling for Retail Choice: Examining a Complex and Crucial Component of Settlement," The Electricity Journal, Elsevier, vol. 13(10), pages 69-74, December.
    5. 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, Elsevier, vol. 24(4), pages 566-587.
    6. Bauwens, Luc & Fiebig, Denzil G & Steel, Mark F J, 1994. "Estimating End-Use Demand: A Bayesian Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 221-31, April.
    7. V. Dordonnat & S.J. Koopman & M. Ooms & A. Dessertaine & J. Collet, 2008. "An Hourly Periodic State Space Model for Modelling French National Electricity Load," Tinbergen Institute Discussion Papers 08-008/4, Tinbergen Institute.
    8. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    9. Amaral, Luiz Felipe & Souza, Reinaldo Castro & Stevenson, Maxwell, 2008. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 603-615.
    10. Vondrácek, Jirí & Pelikán, Emil & Konár, Ondrej & Cermáková, Jana & Eben, Krystof & Malý, Marek & Brabec, Marek, 2008. "A statistical model for the estimation of natural gas consumption," Applied Energy, Elsevier, vol. 85(5), pages 362-370, May.
    11. Cottet R. & Smith M., 2003. "Bayesian Modeling and Forecasting of Intraday Electricity Load," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 839-849, January.
    12. Alves da Silva, Alexandre P. & Ferreira, Vitor H. & Velasquez, Roberto M.G., 2008. "Input space to neural network based load forecasters," International Journal of Forecasting, Elsevier, vol. 24(4), pages 616-629.
    13. Bartels, Robert & Fiebig, Denzil G & Nahm, Daehoon, 1996. "Regional End-Use Gas Demand in Australia," The Economic Record, The Economic Society of Australia, vol. 72(219), pages 319-31, December.
    14. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
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    Cited by:
    1. Haben, Stephen & Ward, Jonathan & Vukadinovic Greetham, Danica & Singleton, Colin & Grindrod, Peter, 2014. "A new error measure for forecasts of household-level, high resolution electrical energy consumption," International Journal of Forecasting, Elsevier, vol. 30(2), pages 246-256.

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