Data-driven Short-term Solar Irradiance Forecasting Based on Information of Neighboring Sites

Short-term forecasting of solar irradiance at a targeted site with consideration of its time-series and measurements at neighboring sites is studied in this paper. A data-driven framework for forecasting solar irradiance based on fusing spatial and temporal information is proposed. In the framework, data-driven approaches including boosted regression trees (BRT), artificial neural network (ANN), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) are applied to model spatial dependence among solar irradiance time-series thereby to generate forecasts of solar irradiance at the targeted site. A comprehensive comparison among data-driven forecasting models is performed using 30-min averaged data of recent two years. Moreover, benchmarking models including scaled persistence (S-PER) model, autoregressive (AR) model, and autoregressive exogenous (ARX) model are employed to further validate the effectiveness of data-driven forecasting models. Computational results of multiple-steps ahead forecasting demonstrate that the BRT model gives the best performance with the lowest normalized root mean squared error (nRMSE) of 18.4%, 24.3%, 27.9%, and 30.6% for forecasting horizons of 30-min, 60-min, 90-min, and 120-min, respectively.