Energy communities — groups of households and businesses that share local renewable energy resources — are emerging as a key mechanism for the energy transition. Realising self-sufficiency within such communities requires intelligent coordination of generation, storage, and consumption. This paper investigates how machine learning can support this coordination by addressing three interrelated problems: short-term solar and demand forecasting, appliance-level load disaggregation via NILM, and community-scale demand response optimisation. We propose an integrated ML pipeline connecting these modules and evaluate it on a simulated energy community with real solar irradiance and consumption data. Results demonstrate that the combined ML approach improves self-sufficiency rates by 18% compared to rule-based scheduling, with forecasting accuracy being the dominant factor in overall community performance.