Abstract
In this empirical study, socioeconomic factors that can easily be extracted from families have been used to build a "home electricity usage prediction" model based on two variables, family monthly income and family size. Each of these factors was evaluated individually. Two machine learning models were built using those factors as features. Models are based on “Linear regression” and “Random Forest” algorithms. This study revealed that the socioeconomic factors such as family size and family income are very effective in domestic electricity usage prediction model building, where the end usages are not known. Furthermore, the random forest algorithm was found to be more effective for unseen data than the linear regression algorithm. The accuracy of the models can be further improved by adding more data into the both models.
Highlights
Electricity is the backbone of modern economies
That means when the power consumption is changed by a unit, about 72% of this change can be predicted by using the two variables, family size and monthly income level
For the given training data, the “Random Forest” algorithm can make the predictions of electricity usage with an r2 value of 0.76 by only taking family income level and number of family members as inputs
Summary
Demand for electrical energy is rapidly increasing in the developing world. An electrical system consists of generating, transmitting, and distributing electrical energy. This process remains complicated and costly, so meeting its increasing demand has become a significant challenge to every nation in the modern world. Domestic electricity demand is one of the essential variables required for estimating the amount of additional capacity required to ensure a sufficient supply of energy. The right grid management strategies should involve load demand planning and an appropriate schedule for generating an effective load distribution. Accurate electricity demand forecasting should be used to maximize the efficiency of the planning and strategy formulation process in the power of domestic distribution systems (Nti, Teimeh, NyarkoBoateng, & Adekoya, 2020). Load prediction and forecasting have become one of the major research fields in electrical engineering
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