Short-Term Solar Irradiance Forecasting Using a Dual Gaussian Process Regression-Particle Filter Framework with Limited Meteorological Measurements

Accurate solar irradiance forecasting can effectively reduce the impact of power fluctuations on the power grid and provide guidance for the control and optimal scheduling of the power system. To address the difficulty of solar irradiance forecasting at sites with limited meteorological measurements, this study proposes a dual Gaussian process regression–particle filter (DGPR-PF) framework. Firstly, a multi-layer correlation analysis method is designed to screen meteorological features while fully considering the coupling relationship between meteorological factors. Secondly, the proposed framework contains two complementary GPR models: a state-transition model that learns the temporal evolution of irradiance from historical irradiance sequences, and an observation model that establishes the nonlinear relationship between irradiance and a small number of available meteorological variables. Temperature and maximum wind speed are selected through multi-layer correlation analysis and used as real-time observations, and a particle filter (PF) recursively fuses the temporal prior generated by the state model with meteorological information from the observation model, thereby correcting irradiance forecasts and reducing the influence of forecasting uncertainty. Finally, the proposed method is validated using the dataset collected by the Yulara Solar System in Australia, and the results indicate that under different seasonal conditions, the proposed method can use a limited number of meteorological factors to achieve a higher forecasting accuracy and has potential for application to incomplete weather measurement systems.