Day-Ahead Solar Forecasting Based on Multi-level Solar Measurements
This addresses the need for more accurate solar forecasting for power system operators due to increased solar deployment, but it is incremental as it builds on existing methods with multi-level data.
The paper tackled the problem of improving day-ahead solar photovoltaic generation forecasting by proposing a model based on multi-level solar measurements and a nonlinear autoregressive with exogenous input (NARX) model, achieving acceptable performance compared to two-level and single-level studies under different weather conditions.
The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and achieve better forecasts. The proposed model consists of four stages of data preparation, establishment of fitting model, model training, and forecasting. The model is tested under different weather conditions. Numerical simulations exhibit the acceptable performance of the model when compared to forecasting results obtained from two-level and single-level studies.