Point and interval forecasting of solar irradiance with an active Gaussian process

Abstract

A Gaussian process regression (GPR) with active learning is proposed for developing the solar irradiance point and interval forecasting models, which consider the spatial-temporal information collected from a targeted site and a number of neighbouring sites. To enhance the performance of the GPR-based model an active learning process is developed for constructing an ad-hoc input feature set, selecting training data points, and optimising hyper-parameters of GPR models. To validate the advantages of the proposed method, a comprehensive computational study is conducted based on solar irradiance data collected from the northwest California area. In the point forecasting, the proposed method beats the state-of-the-art benchmarking methods including classical statistical models and data-driven models according to values of the normalised root mean squared error, normalised mean absolute error, normalised mean bias error, and coefficient of determination. In the interval forecasting, the proposed method outperforms the persistence model, autoregressive model with exogenous inputs, generic GPR, as well as two recently reported forecasting methods, the bootstrap-based extreme learning machine and quantile regression, in terms of the forecasting reliability. Computational results show that the proposed method is more effective than well-known existing benchmarks in the point and interval forecasting of the solar irradiance.