Operational Planning of a Home Energy Management System Using Regional Weekly Weather Forecasts to Mitigate Surplus Electricity

Photovoltaic (PV) systems often generate surplus electricity during daytime when production exceeds demand. To address this, existing studies optimize energy storage and heat-pump (HP) water heater operations but typically focus only on same-day forecasts. This study proposes using regional weekly weather forecasts to enhance PV surplus management. Solar irradiance is estimated via machine learning trained on historical data, using daily and weekly forecasts as inputs. Predicted irradiance informs PV generation forecasts, guiding optimal operational planning for battery storage and HP water heaters through linear programming. Plans are adjusted based on actual generation data. Results indicate that perfectly accurate weekly forecasts could reduce surplus electricity by 16% compared to same-day forecasts. Even with estimated irradiance, integrating next-day forecasts reduces surplus by 0.66% relative to same-day predictions alone.