Short-term probabilistic forecasts for Direct Normal Irradiance

Abstract

A k-nearest neighbor (kNN) ensemble model has been developed to generate Probability Density Function (PDF) forecasts for intra-hour Direct Normal Irradiance (DNI). This probabilistic forecasting model, which uses diffuse irradiance measurements and cloud cover information as exogenous feature inputs, adaptively provides arbitrary PDF forecasts for different weather conditions. The proposed models have been quantitatively evaluated using data from different locations characterized by different climates (continental, coastal, and island). The performance of the forecasts is quantified using metrics such as Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Averaged Width (PINAW), Brier Skill Score (BSS), and the Continuous Ranked Probability Score (CRPS), and other standard error metrics. A persistence ensemble probabilistic forecasting model and a Gaussian probabilistic forecasting model are employed to benchmark the performance of the proposed kNN ensemble model. The results show that the proposed model significantly outperform both reference models in terms of all evaluation metrics for all locations when the forecast horizon is greater than 5-min. In addition, the proposed model shows superior performance in predicting DNI ramps.