Quantile mapping correction of analog ensemble forecast for solar irradiance

Abstract

Probabilistic forecasts of solar irradiance are a key technology for integrating solar power into power grids. However, ensemble forecasts, the conventional probabilistic forecasting method suffers from bias inherent in numerical weather prediction. The recently proposed Analog Ensemble (AnEn) method is expected to have less bias. We therefore construct an experimental AnEn system for day-ahead solar irradiance forecasts and investigate its performance. The AnEn method finds analogs by a nearest-neighbor search, so its members can be ranked by the order found in the search. We find that the probability distribution function (PDF) for predicted irradiance deduced from members with large orders are heavily deformed from the desired PDF, namely, that of the observed solar irradiance. This induces bias in AnEn forecasts limiting their performance. The bias can be ascribed to the limited size of data available in the AnEn forecast system. In many practical cases, this limitation is unavoidable. We propose a correction scheme based on quantile mapping to deal with this situation. By enhancing forecast reliability, this scheme provides better continuous ranked probability scores compared with the basic AnEn method. However, PDF estimation in the quantile mapping process can be unstable. We therefore propose another scheme that introduces constraints to mitigate this uncertainty thereby achieving further performance gains.