Reliable Prediction Intervals of PV Generation Using Quantile Regression Averaging Approach

Abstract

Probabilistic PV generation forecasts are necessary for the uncertainty management in the long-term planning of power systems with PV integrations. The weather-dependent PV generation makes it a challenging task necessitating a nonparametric approach, such as quantile regression, for obtaining probabilistic forecasts. Here, a quantile regression averaging approach is used to combine the selective point forecasts of autoregressive conditional heteroscedastic model, random forests model, and multiple linear regression model to further enhance the forecast accuracy. The selection of sensible regressors with physical relevance is necessary for the proposed framework. Hence, such theoretically formulated regressors capable of modeling real-world PV generation data collected from the USA are utilized for assessing the efficacy of the proposed quantile regression averaging model. The reliabilities of the prediction intervals of the proposed model is compared with the popular quantile regression forests, the quantile k-nearest neighbors, and the basic quantile regression approaches via widely used performance indices.