Operational Value of Load Forecasting for Battery Dispatch and Energy Sharing in Renewable Energy Communities

Residential batteries in energy communities usually run local rule-based controllers whose community-level effect is rarely checked against measured data. This study uses one year of smart-meter, PV and battery records from a real 20-household Irish community (24 kWp PV, 200 kWh storage) to test how much can be achieved with locally available data only. The existing controllers charged all batteries in the same night-tariff hours. With household flows netted hourly at the community level, this raised grid import by 14.5% and CO2 emissions by 15.8% compared with no batteries, raised the maximum hourly import from 33 to 83 kW, and created a new morning peak. A lightweight framework, combining a drift-adaptive LightGBM load forecast and a mixed-integer dispatch with real-time correction, reduced grid import by 10.5–13.5%, emissions by 11.6–14.6%, and energy imported above a 14.1 kW reference threshold by 87% relative to the measured operation, with each scenario computed in seconds. A forecast-free controller acting on the same threshold reduced this exceedance at least as much, while the forecast-based optimisation lowered the import by a further 2.6%. LightGBM was 10.9% more accurate than a seven-day average, yet its dispatch did not differ significantly (Wilcoxon p = 0.53); correcting the systematic underestimation of daily peaks reduced the energy above the threshold by 14%. Sharing PV reduced imports by 9.7% and benefited mainly households without PV, whereas pooled storage mainly relieved the grid, with the effect increasing with better information.