Abstract
Electricity is one of the most widely used forms of
energy that plays a significant part in sufficing the fundamental
energy demand based on contemporary human needs. It is
becoming highly tedious for the energy sector to manage and
surveil the modern energy demands based on the constantly
changing consumer demographics. In order to progress as a
business, it has become pertinent for the distribution companies
to evolve their development plans, tariffs, and business models
according to the consumer requirements. This article proposes
three different models to predict the attributes of a consumer
household, namely the multivariate linear regression (MLR), the
support vector regression (SVR), and the artificial neural network
(ANN). The study uses the PRECON dataset, which is based on
the monthly electricity consumption of households in Lahore,
Pakistan. All of the proposed models play significant roles in
predicting the required consumer demographics for forecasting.
The linear model shows the ability to predict the number of
people with very low MAPE of 3.57% as compared to other
models. So far, ANN has shown the best results in predicting
the number of fans, air conditioners, and rooms. However, the
MAPE reports extracted from this study show the inability of the
used models to explain the variation of property area confidently.