A predictive model for power consumption estimation using machine learning full stack with web development

Abstract

Power consumption forecasting plays a crucial role in smart grid management, energy optimization, and sustainable development. This project presents a predictive model for estimating power consumption using machine learning integrated with a full-stack web application. Historical energy usage data is collected, preprocessed, and used to train regression-based machine learning models. The trained model is deployed using a backend framework and accessed through a web-based user interface. Real-time prediction capability enables users to analyze consumption trends and future demand efficiently. The system improves decision-making for energy providers and consumers. Experimental results demonstrate high prediction accuracy and reduced error rates. This approach supports scalable, intelligent energy management systems.