Short-term irradiance forecastability for various solar micro-climates

The purpose of this work is to present a simple global solar irradiance forecasting framework based on the optimization of the k-nearest-neighbors (kNN) and artificial neural networks algorithms (ANN) for time horizons ranging from 15 min to 2 h. We apply the proposed forecasting models to irradiance from five locations and assessed the impact of different micro-climates on forecasting performance. We also propose two metrics, the density of large irradiance ramps and the time series determinism, to characterize the irradiance forecastability. Both measures are computed from the irradiance time series and provide a good indication for the forecasting performance before any predictions are produced. Results show that the proposed kNN and ANN models achieve substantial improvements relative to simpler forecasting models. The results also show that the optimal parameters for the kNN and ANN models are highly dependent on the different micro-climates. Finally, we show that the density of large irradiance ramps and time series determinism can successfully explain the forecasting performance for the different locations and time horizons.