Spatio-Temporal Resolution of Irradiance Samples in Machine Learning Approaches for Irradiance Forecasting

Improving short term solar irradiance forecasting is crucial to increase the market share of the solar energy production. This paper analyzes the impact of using spatially distributed irradiance sensors as inputs to four machine learning algorithms: ARX, NN, RRF and RT. We used data from two different sensor networks for our experiments, the NREL dataset that includes data from 17 sensors that cover a <inline-formula> <tex-math notation="LaTeX">$1~km^{2}$ </tex-math></inline-formula> area and the InfoRiego dataset which includes data from 50 sensors that cover an area of <inline-formula> <tex-math notation="LaTeX">$94~Km^{2}$ </tex-math></inline-formula>. Several studies have been published that use these datasets individually, to the author knowledge this is the first work that evaluates the influence of the spatially distributed data across a range from 0.5 to 17 sensors per <inline-formula> <tex-math notation="LaTeX">$km^{2}$ </tex-math></inline-formula>. We show that all of algorithms evaluated are able to take advantage of the data from the surroundings, from the very short forecast horizons of 10s up to a few hours, and that the wind direction and intensity plays an important role in the optimal distribution of the network and its density. We show that these machine learning methods are more effective on the short horizons when data is obtained from a dense enough network to capture the cloud movements in the prediction interval, and that in those cases complex non-linear models give better results. On the other hand, if only a sparse network is available, the simpler linear models give better results. The skills obtained with the models under test range from 13% to 70%, depending on the sensor network density, time resolution and lead time.