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Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil

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  • Cabral, Joilson de Assis
  • Freitas Cabral, Maria Viviana de
  • Pereira Júnior, Amaro Olímpio

Abstract

Spatiotemporal models to estimate electricity demand are scarce in the existing literature. In this paper, we compare three models to estimate elasticities and forecast demand for residential electricity in Brazil. The Dynamic Spatial Durbin Model presented the best goodness of fit, with results that confirm the need to consider spatial dependence in the Brazilian regions. The results showed temporal inertia, inelasticity of demand concerning price and income, and a significant impact of the temperature and the number of households connected to the grid. We conclude that omitting the spatiotemporal dynamic could lead to bias in the models used by Brazilian utilities.

Suggested Citation

  • Cabral, Joilson de Assis & Freitas Cabral, Maria Viviana de & Pereira Júnior, Amaro Olímpio, 2020. "Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil," Utilities Policy, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:juipol:v:66:y:2020:i:c:s0957178720301028
    DOI: 10.1016/j.jup.2020.101108
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    2. Zuin, Gianlucca & Buechler, Rob & Sun, Tao & Zanocco, Chad & Galuppo, Francisco & Veloso, Adriano & Rajagopal, Ram, 2023. "Extreme event counterfactual analysis of electricity consumption in Brazil: Historical impacts and future outlook under climate change," Energy, Elsevier, vol. 281(C).
    3. Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
    4. Nor Salwati Othman & Nurul Hezlin Mohamed Hariri, 2021. "Estimating the Causality and Elasticities of Residential Electricity Consumption for Malaysia," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 335-346.
    5. Sen, Doruk & Hamurcuoglu, K. Irem & Ersoy, Melisa Z. & Tunç, K.M. Murat & Günay, M. Erdem, 2023. "Forecasting long-term world annual natural gas production by machine learning," Resources Policy, Elsevier, vol. 80(C).
    6. Shiwen Liu & Zhen Zhang & Junhua Yang & Wei Hu, 2022. "Exploring Increasing Urban Resident Electricity Consumption: The Spatial Spillover Effect of Resident Income," Energies, MDPI, vol. 15(12), pages 1-17, June.
    7. Ciarreta, Aitor & Martinez, Blanca & Nasirov, Shahriyar, 2023. "Forecasting electricity prices using bid data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1253-1271.

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