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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

23 Scopus Citations
View graph of relations



Original languageEnglish
Pages (from-to)1536-1547
Journal / PublicationIEEE Transactions on Sustainable Energy
Issue number4
Online published17 Apr 2017
Publication statusPublished - Oct 2017


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.

Research Area(s)

  • Bayesian inference, Markov-chain, regime switching, solar radiance prediction, time series