Probabilistic forecasting of high-resolution clear-sky index time-series using a Markov-chain mixture distribution model

This study presents a Markov-chain mixture (MCM) distribution model for forecasting the clear-sky index-normalized global horizontal irradiance. The model is presented in general, but applied to, and tested or minute resolution clear-sky index data for the two different climatic regions of Norrkoping, Sweden, and Hawaii USA. Model robustness is evaluated based on a cross-validation procedure and on that basis a reference con figuration of parameter settings for evaluating the model performance is obtained. Simulation results ar compared with persistence ensemble (PeEn) and quantile regression (QR) model simulations for both data set and for D = 1,...,5 steps ahead forecasting scenarios. The results are evaluated by a set of probabilistic fore casting metrics: reliability mean absolute error (reliability MAE), prediction interval normalized average widti (PINAW), continuous ranked probability score (CRPS) and continuous ranked probability skill score (skill). Botl in terms of reliability MAE and CRPS, the MCM model outperforms PeEn for all simulated scenarios. In terms c reliability MAE, the QR model outperforms the MCM model for most simulated scenarios. However, in terms c mean CRPS, the MCM model outperforms the QR model in most simulated scenarios. A point forecasting esti mate is also provided. The MCM model is concluded to be a computationally inexpensive, accurate and pars meter insensitive probabilistic model. Based on this, it is suggested as a candidate benchmark model in prop abilistic forecasting, in particular for solar irradiance forecasting. For applicability, a Python script of the MCA model is available as SheperoMah/MCM-distribution-forecasting at GitHub.