No abstract

No abstract

No abstract

Photovoltaic (PV) power forecasting has the potential to mitigate some of effects of\ud resource variability caused by high solar power penetration into the electricity grid. Two\ud main methods are currently used for PV power generation forecast: (i) a deterministic\ud approach that uses physics-based models requiring detailed PV plant information and\ud (ii) a data-driven approach based on statistical or stochastic machine learning techniques\ud needing historical power measurements. The main goal of this work is to analyze\ud the accuracy of these different approaches. Deterministic and stochastic models for dayahead\ud PV generation forecast were developed, and a detailed error analysis was performed.\ud Four years of site measurements were used to train and test the models. Numerical\ud weather prediction (NWP) data generated by the weather research and forecasting\ud (WRF) model were used as input. Additionally, a new parameter, the clear sky performance\ud index, is defined. This index is equivalent to the clear sky index for PV power generation\ud forecast, and it is here used in conjunction to the stochastic and persistence models.\ud The stochastic model not only was able to correct NWP bias errors but it also provided a\ud better irradiance transposition on the PV plane. The deterministic and stochastic models\ud yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively

No abstract