Wind Influence on the Spatiotemporal Forecast of Global Horizontal Irradiance

Abstract

The connection between solar irradiance and wind is a topic of interest in the field of renewable energies, as wind data have proven to be effective predictors of solar energy, being indicators of cloud movement and atmospheric conditions. This study focuses the use of decision tree-based algorithms (random forest, XGBoost and light gradient boosting machine, and LightGBM) to analyse the impact of the meridional and zonal wind components as input variables. In the study, past observations of neighbours were included as predictors to include a spatiotemporal analysis. The studied models were trained on the open, well-established OIH dataset (containing data from Oahu Island, Hawaii, located in the United States of America) featuring predominantly northeasterly winds. In the post-training analysis, it was found that the inclusion of the wind components resulted in a mean improvement of approximately 1% in the forecast skill (FS) score for all models, with the XGBoost model being the best performing model (with a 27.63% FS score).

Keywords

• wind
• global horizontal irradiance forecast
• machine learning
• spatiotemporal approach