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.
@article{arxiv.1901.07525,
title = {Model Estimation for Solar Generation Forecasting using Cloud Cover Data},
author = {Daniele Pepe and Gianni Bianchini and Antonio Vicino},
journal= {arXiv preprint arXiv:1901.07525},
year = {2024}
}