Abstract

Energy consumption prediction plays a critical role in optimizing energy usage, reducing waste, and ensuring the sustainability of power grids. With the growing use of smart meters, sensors, and IoT devices, there is a wealth of real-time data that can be leveraged to predict energy usage patterns. This paper explores the application of machine learning (ML) algorithms in predicting energy consumption, focusing on both residential and industrial settings. By utilizing supervised and unsupervised learning techniques, we demonstrate how ML can provide accurate energy consumption forecasts and improve decision-making for energy management systems.