Model estimation for solar generation forecasting using cloud cover data

•A novel parametric model approach to PV generation forecasting is presented.•Raw cloud cover data combined with generation measurements are exploited.•On-site measurements of meteorological variables are not required.•The method is computationally efficient and suited for large-scale PV integration.•Extensive validation is performed in simulated scenarios and on real data. This paper presents a parametric model approach to address the problem of photovoltaic generation forecasting in a scenario where measurements of meteorological variables, i.e., solar irradiance and temperature, are not available at the plant site. This scenario is relevant to electricity network operation, when a large number of PV plants are deployed in the grid. The proposed method makes use of raw cloud cover data provided by a meteorological service combined with power generation measurements, and is particularly suitable in PV plant integration on a large-scale basis, due to low model complexity and computational efficiency. An extensive validation is performed using both simulated and real data.