Prediction of Energy Consumption by Using Machine Learning

The increasing global demand for electricity, fueled by urbanization, industrialization, and technological advancements, has created a need for efficient energy management systems. Traditional electricity billing methods, which typically rely on periodic meter readings, often fail to capture the nuanced factors that influence electricity consumption. This paper explores the application of Supervised Machine Learning (SML) techniques, such as Linear Regression and Ridge Regression, to predict electricity bills more accurately by integrating contextual factors such as weather data, time of day, and seasonal variations. By utilizing previous energy consumption data along with contextual features, the proposed models aim to generate reliable estimates of future electricity bills, empowering utilities, consumers, and policymakers to optimize energy resources.This study evaluates the predictive performance of the two models and compares them with traditional methods, such as time-series analysis. It aims to identify the most effective forecasting model while assessing the practical implications for utility companies and consumers. The findings highlight the role of accurate billing in reducing costs and promoting sustainability. By offering more precise electricity predictions, this research contributes to enhanced decision-making in resource allocation, energy efficiency initiatives, and policy development within the energy sector