HOUSEHOLD POWER CONSUMPTION ESTIMATION USING MACHINE LEARNING

Abstract

With rising electricity demand across domestic and industrial sectors and the growth of smart meter infrastructure, accurate energy consumption forecasting has become essential. Intelligent residential buildings, though convenient with remote device control, often lead to increased energy usage. To address this, an ensemble regression model combining linear prediction and Support Vector Regression (SVR) is used to enhance demand forecasting. Factors such as climate, building materials, and systems for heating, lighting, and ventilation significantly influence consumption. By utilizing historical data and advanced predictive algorithms, energy management can be optimized to reduce waste and improve efficiency across various settings.