Explaining household electricity consumption using quantile regression, decision tree and artificial neural network
Jean Calvin Nsangou,
Paul Salomon Ngohe Ekam,
Joseph Voufo and
Thomas T. Tamo
Energy, 2022, vol. 250, issue C
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.
Keywords: Statistical modelling; Quantile regression; Decision tree; Artificial neural network; Electricity demand; Cameroon (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:250:y:2022:i:c:s0360544222007599
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().