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Explaining household electricity consumption using quantile regression, decision tree and artificial neural network

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  • Nsangou, Jean Calvin
  • Kenfack, Joseph
  • Nzotcha, Urbain
  • Ngohe Ekam, Paul Salomon
  • Voufo, Joseph
  • Tamo, Thomas T.

Abstract

Electricity as an energy carrier par excellence has a vital role in economic development. However, even with the transformation of power systems that follows technological development, mastering the electricity consumption dynamics remains a challenge task. Therefore, in order to be able to develop effective energy policies based on accurate projections, the search for better consumption modelling options is nowadays the focus of many researchers. Given the emerging use of quantile regression, decision tree and artificial neural network models for prediction, this study tries to address the issue of their performances in assessing electricity consumption drivers. A sample of data from a household's electricity consumption survey in Cameroon was used as the empirical context for application of these three models through a comparative analysis. Factors related to equipment and their use, household income level, housing structure, residential living, energy-saving behaviour and weather conditions showed a significant influence on electricity consumption. Artificial neural network model proved to be more efficient than quantile regression and decision tree, with root mean square error (RMSE), mean absolute error (MAE), and mean absolute error percentage (MAPE) lower values and a higher determination coefficient (R2) value. It is expected that these results serve as a reference for making a decision when selecting the most accurate approach for better understanding of those drivers that have the greatest impact on energy demand.

Suggested Citation

  • Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:energy:v:250:y:2022:i:c:s0360544222007599
    DOI: 10.1016/j.energy.2022.123856
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