International Journal of Environmental Engineering and Development
E-ISSN: 2945-1159
Volume 3, 2025
Weather-Integrated Smart Energy Administration Forecasting and Cost Estimation Powered in Artificial Intelligence
Authors: ,
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Abstract: 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.
Keywords:
Energy Prediction, Machine Learning, XGBoost, Smart Metering, Weather-Based Energy Analysis
Pages: 66-70
DOI: 10.37394/232033.2025.3.6