Weather-Integrated Smart Energy Administration Forecasting and Cost Estimation Powered in Artificial Intelligence

An AI-powered method for forecasting energy consumption (kWh) and electricity expenses based on appliance usage and current weather conditions is presented in this research. The suggested technique employs a dynamic slab-based tariff system to precisely calculate power bills and incorporates XGBoost-based machine learning to assess energy usage. The technology is perfect for smart homes and energy-efficient decision-making since it dynamically modifies energy use based on temperature, humidity, and wind speed to increase prediction accuracy. According to the experimental findings, the system's prediction error is less than 5% of the actual measurements.