We consider the problem of estimating the (unobserved) amounts of PV generation and demand in a power distribution network starting from measurements of the aggregated power flow at the point of common coupling (PCC) by leveraging local global horizontal irradiance (GHI) measurements. The estimation principle consists in modeling the PV generation as a function of the measured GHI, enabling to identify PV production patterns in the aggregated power flow measurements. Four estimation algorithms are proposed: the first assumes that variability in the aggregated PV generation is mostly given by variations of PV generation, two use a model of the demand to improve estimation performance, and the fourth assumes that, in a certain frequency range, the aggregated power flow is dominated by PV generation dynamics. Algorithms take advantage of irradiance transposition models to explore several azimuth/tilt configurations and explain PV generation patterns from the aggregated power flow profile accounting for non-uniform configurations of the plants. Algorithms estimation performance is compared and validated by using experimental measurements from a real-life setup with 4 dwellings with rooftop PV installations and battery systems for self-consumption.