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Modeling and Short-term Forecasting of New South Wales Electricity System Load

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  • Smith, Michael

Abstract

This article employs Bayesian semiparametric regression methodology to model intraday electricity load data and obtain short-term load forecasts. The role of such forecasts in the New South Wales wholesale electricity market is discussed and the method applied to New South Wales system load data. The semiparametric regression model used identifies daily periodic, weekly periodic, and temperature-sensitive components of load. Each component is decomposed as a linear combination of basis functions, with a nonzero probability mass that the corresponding coefficients are exactly zero. Three possible models for the errors are also considered, including independent, autoregressive, and first-differenced autoregressive models. A moving window of data is used to overcome the slow time-varying nature of the temperature and periodic effects. The entire model is estimated using a Bayesian Markov chain Monte Carlo approach, and forecasts are obtained using a Monte Carlo sample from the joint predictive distribution of future system load. It is demonstrated how accurate temperature forecasts can result in accurate intraday system load forecasts for even quite long forecast horizons.

Suggested Citation

  • Smith, Michael, 2000. "Modeling and Short-term Forecasting of New South Wales Electricity System Load," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 465-478, October.
  • Handle: RePEc:bes:jnlbes:v:18:y:2000:i:4:p:465-78
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    Cited by:

    1. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
    2. Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S., 2013. "A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting," International Journal of Forecasting, Elsevier, vol. 29(1), pages 1-12.
    3. Rong Chen & John L. Harris & Jun M. Liu & Lon-Mu Liu, 2006. "A semi-parametric time series approach in modeling hourly electricity loads," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(8), pages 537-559.
    4. Do, Linh Phuong Catherine & Lin, Kuan-Heng & Molnár, Peter, 2016. "Electricity consumption modelling: A case of Germany," Economic Modelling, Elsevier, vol. 55(C), pages 92-101.
    5. Tristan Launay & Anne Philippe & Sophie Lamarche, 2015. "Construction of an informative hierarchical prior for a small sample with the help of historical data and application to electricity load forecasting," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 361-385, June.
    6. 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.
    7. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    8. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, vol. 24(4), pages 710-727.
    9. Rafal Weron & Adam Misiorek, 2005. "Modeling and forecasting electricity loads: A comparison," Econometrics 0502004, EconWPA.
    10. Kim, Myung Suk, 2013. "Modeling special-day effects for forecasting intraday electricity demand," European Journal of Operational Research, Elsevier, vol. 230(1), pages 170-180.
    11. Bakhat, Mohcine & Rosselló, Jaume, 2011. "Estimation of tourism-induced electricity consumption: The case study of Balearics Islands, Spain," Energy Economics, Elsevier, vol. 33(3), pages 437-444, May.
    12. Espasa, Antoni & Cancelo, José Ramón & Grafe, Rosmarie, 2007. "Forecasting from one day to one week ahead for the Spanish system operator," DES - Working Papers. Statistics and Econometrics. WS ws078418, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Jaume Rosselló Nadal & Mohcine Bakhat, 2009. "A new approach to estimating tourism-induced electricity consumption," CRE Working Papers (Documents de treball del CRE) 2009/6, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    14. Smith, Michael & Kohn, Robert, 2000. "Nonparametric seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 98(2), pages 257-281, October.
    15. 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.
    16. Joanna Nowicka-Zagrajek & Rafal Weron, 2002. "Modeling electricity loads in California: ARMA models with hyperbolic noise," HSC Research Reports HSC/02/02, Hugo Steinhaus Center, Wroclaw University of Technology.

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