Analysis of Home Energy Management System with Probabilistic Solar Power Forecasting

Residential Energy management has become crucial to optimize energy usage, reduce electricity costs, and seamlessly integrate renewable energy sources within households. With the increasing integration of renewable energy sources, effective forecasting of solar photovoltaic generation has become crucial for optimal energy management. Deterministic forecasting assumes precise and accurate knowledge of PV generation, while probabilistic forecasting incorporates uncertainty by presenting a range of potential outcomes. This study investigates the impact of the above forecasting methodologies on energy cost savings, grid reliance, and load balancing in a Home Energy Management System. The results indicate that probabilistic forecasting improves cost efficiency by 68.2 %, reduces grid dependence, and enhances self-sufficiency by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 7. 7 \%}$</tex>, particularly under cloudy conditions.