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Forecasting day-ahead electricity load using a multiple equation time series approach

Author

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  • Clements, A.E.
  • Hurn, A.S.
  • Li, Z.

Abstract

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.

Suggested Citation

  • Clements, A.E. & Hurn, A.S. & Li, Z., 2016. "Forecasting day-ahead electricity load using a multiple equation time series approach," European Journal of Operational Research, Elsevier, vol. 251(2), pages 522-530.
  • Handle: RePEc:eee:ejores:v:251:y:2016:i:2:p:522-530
    DOI: 10.1016/j.ejor.2015.12.030
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    References listed on IDEAS

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    1. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    2. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    3. Gould, Phillip G. & Koehler, Anne B. & Ord, J. Keith & Snyder, Ralph D. & Hyndman, Rob J. & Vahid-Araghi, Farshid, 2008. "Forecasting time series with multiple seasonal patterns," European Journal of Operational Research, Elsevier, vol. 191(1), pages 207-222, November.
    4. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    5. 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.
    6. 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.
    7. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    8. Engle, R. F. & Granger, C. W. J. & Hallman, J. J., 1989. "Merging short-and long-run forecasts : An application of seasonal cointegration to monthly electricity sales forecasting," Journal of Econometrics, Elsevier, vol. 40(1), pages 45-62, January.
    9. 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.
    10. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    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. 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.
    13. Peirson, John & Henley, Andrew, 1994. "Electricity load and temperature : Issues in dynamic specification," Energy Economics, Elsevier, vol. 16(4), pages 235-243, October.
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    Cited by:

    1. repec:eee:intfor:v:34:y:2018:i:4:p:566-581 is not listed on IDEAS
    2. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
    3. Moisan, Stella & Herrera, Rodrigo & Clements, Adam, 2018. "A dynamic multiple equation approach for forecasting PM2.5 pollution in Santiago, Chile," International Journal of Forecasting, Elsevier, vol. 34(4), pages 566-581.
    4. repec:eee:energy:v:159:y:2018:i:c:p:534-546 is not listed on IDEAS
    5. repec:eee:ejores:v:269:y:2018:i:3:p:1165-1179 is not listed on IDEAS
    6. repec:eee:eneeco:v:67:y:2017:i:c:p:60-71 is not listed on IDEAS
    7. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    8. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    9. repec:gam:jeners:v:10:y:2017:i:11:p:1701-:d:116309 is not listed on IDEAS
    10. repec:eee:eneeco:v:74:y:2018:i:c:p:886-903 is not listed on IDEAS

    More about this item

    Keywords

    Short-term load forecasting; Modelling seasonality; Intra-day load correlation;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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