Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations

Abstract

The effective use of solar photovoltaic (PV) installations implies the integration of solar PV output into overall energy consumption planning, optimization, and control. Moreover, day-ahead trading of electricity in Europe makes day-ahead solar PV forecasting utterly important, and thus its accuracy becomes of particular interest. Data-driven PV forecasting models are typically trained using numerical weather prediction (NWP) data, the availability of which represents one of the main obstacles in modeling. In this study, we investigate an alternative scenario, in which an artificial neural network (ANN) is trained on weather observations and then tested on NWP data to simulate the model's use in operational PV forecasting. In the experiments, solar PV output data, historical weather observations, and historical NWP data were collected from three sites in eastern Finland. The results showed that, although training ANN on observational data leads to a slight decrease in its performance compared to ANN trained on NWP data, it still outperforms a physical model. In practice, this alternative scenario means that if historical NWP data are not available for model training, observational data allow effective model selection and parameter tuning, and then generalization error estimates are gradually updated using online NWP data.