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Modeling and Forecasting Hourly Electricity Demand by SARIMAX with Interactions

Author

Listed:
  • Niematallah Elamin

    (Graduate School of Economics, Osaka University)

  • Mototsugu Fukushige

    (Graduate School of Economics, Osaka University)

Abstract
This paper presents an interaction forecasting framework with a focus on short-term load forecasting. It proposes a seasonal autoregressive integrated moving average model with the inclusion of exogenous variables (SARIMA: main effects) and interaction variables (cross effects) to forecast short-term electricity load using hourly load data from Tokyo Electric Power Company. The main effects and cross effects are measured through an iterative process of plotting, interpreting, and testing. Interactions of weather variables and calendar variables, as well as interactions of seasonal patterns and intraday dependencies, are analyzed, tested, and added to the model. We compare the SARIMAX model, which contains only main effects, with the Interaction-SARIMAX model, which includes cross effects in addition to the main effects. Our proposed SARIMAX-with-interactions model is shown to produce smaller errors than its competitors. Thus, including interaction effects of the exogenous variables into the SARIMAX model can potentially improve the model forecasting performance. Although the model is built using data of a specific region in Japan, the method is completely generic and therefore applicable to any load forecasting problem.

Suggested Citation

  • Niematallah Elamin & Mototsugu Fukushige, 2017. "Modeling and Forecasting Hourly Electricity Demand by SARIMAX with Interactions," Discussion Papers in Economics and Business 17-28, Osaka University, Graduate School of Economics.
    • Handle: RePEc:osk:wpaper:1728
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    References listed on IDEAS

    as
    1. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
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    5. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.
    6. 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.
    7. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    8. 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.
    9. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
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    More about this item

    Keywords

    Cross effects; forecast accuracy; load forecasting; load modeling; SARIMAX;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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