An Ensemble Learning based Energy Forecasting Model: A Sustainable Home Energy Management System for Smart City

In the era of smart city, energy and its management distribution, sustainability, and cost-effectiveness. Due to improper energy management, many cities around the world suffer from regular power cuts, low voltage, or both. At the same time, grid management systems also struggle with static or semi-dynamic energy management schedules. We propose a novel Forecast-based Home Energy Management System Optimization model (FHEMSO) that leverages advanced predictive analytics and artificial intelligence to optimize energy distribution among smart homes in a smart city. The proposed model integrates weather forecasts, real-time energy usage data, and usage patterns to predict energy demand accurately and manage energy distribution dynamically. The objective of FHEMSO is to reduce energy wastage, reduce carbon footprints, and enhance energy efficiency. The proposed model not only benefits individual households by minimizing energy bills, but also contributes to sustainable energy practices. Through extensive simulations, the effectiveness of FHEMSO in achieving energy savings and quality energy distribution is demonstrated. The proposed FHEMSO model is simulated over Kaggle Smart-Home-Dataset and efficacy is compared with existing models.