Day-Ahead Prediction of Bihourly Solar Radiance With a Markov Switch Approach

Abstract

A Bayesian inference based Markov regime switching model is introduced to predict the intraday solar radiance. The proposed model utilizes a regime switching process to describe the evolution of the solar radiance time series. The optimal number of regimes and regime-specific parameters are determined by the Bayesian inference. The Markov regime switching model provides both the point and interval prediction of solar radiance based on the posterior distribution derived from historical data by the Bayesian inference. Four solar radiance forecasting models, the persistence model, the autoregressive (AR) model, the Gaussian process regression (GPR) model, and the neural network (NN) model, are considered as baseline models for validating the Markov switching model. The comparative analysis based on numerical experiment results demonstrates that in general the Markov regime switching model performs better than compared models in the day-ahead point and interval prediction of the solar radiance.