Explaining Household Electricity Consumption Using Quantile Regression, Decision Tree and Artificial Neural Network

In order to be able to develop effective energy policies based on accurate projections, the search for better consumption modeling options is nowadays the focus of many researchers. Given the emerging use of quantile regression, decision tree and artificial neural network models for energy demand prediction, this study tries to address the issue of their performances in modeling electricity consumption. 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 behavior 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) 1 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 understanding the dynamics of energy demand and saving options