International Journal of Science and Technology (IJST)

AI-Enabled Smart Energy Optimization System for Consumption Forecasting and Cost Reduction

Managing household electricity consumption in India is challenging due to complex slab-based tariff structures and limited consumer awareness of appliance-level energy usage. This paper presents a Next Generation Smart Energy Optimizer, an AI-driven web-based system designed to predict electricity consumption and optimize household energy costs. The proposed system integrates machine learning, a slab-aware billing engine, and large language model (LLM)-based recommendations to provide comprehensive decision support. A Random Forest regression model, trained on real-world household data, predicts appliance-wise monthly energy consumption with high accuracy. The billing engine simulates real Distribution Company (DISCOM) tariff structures, generating detailed cost breakdowns including energy charges, fixed charges, and applicable duties. Additionally, an LLM-based advisory module produces personalized and context-aware recommendations to help users reduce consumption and expenses. The system is implemented using a Python–Flask backend, PostgreSQL database, and a browser-based frontend, ensuring scalability and accessibility. Experimental evaluation shows strong performance, achieving a mean absolute percentage error (MAPE) of 6.8% compared to actual electricity bills. The proposed framework bridges the gap between consumption prediction and actionable insights, offering a practical solution for intelligent household energy management.