Observation Modeling for Continuous Predicting Global and Diffuse Solar Radiation on Horizontal Surface

Abstract

Mathematical modeling for continuous prediction of solar energy is inevitable for energy systems. General, existing models are mostly empirical and data dependent. This article has to present a variable model for predicting global and diffuse solar radiation on the horizontal plane. The model considered in this study has applied Hidden Markov Model (HMM) with two observations. The databases were measured according CIE standard since 2004 to 2010 for model synthetics. The new data of 2011 year used for testing model. Training, take data in time sequence and clustering. After that, create transition probability in each state have two observations matrix; Sky Ratio (SR) and solar altitude angle (α) are observation value of the model. Predicting, values of solar radiation are considered as the hidden events by will been calculate probability of observation from P [SR ∩ α], state as highest probability was selected for predict state and convert to solar radiation quantity. Model evaluation, three statistical namely MBD, RMSD and R2were used for model evaluations. The results show that, this model is appropriate for predicting sky quantities. Conclusion, the variable model from the synthesis was suitable for predicting long-term data with observation value. The advantage of this study showed that we could predict sky the quantity value when we only knew the observation values.