Marco Pierro

Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting

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

Multi-Model Ensemble for Day Ahead PV Power Forecasting Improvement

aDepartment of Enterprise Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy, e-mail: cornaro@uniroma2.it, marco.pierro@gmail.com, frabucci@gmail.com bCHOSE, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy 7 cIdeam Srl, via Frova 34 Cinisello Balsamo, Italy, e-mail: alessandro.perotto, enrico.maggioni, francesco.spada@ideamweb.com dCasaccia R.C., ENEA Climate Modelling Laboratory, Rome, Italy e-mail: matteo.defelice@enea.it eEURAC Research, Viale Druso, 1, 39100 Bolzano, Italy e-mail: david.moser@eurac.edu