Statistical estimation of hourly diffuse radiation intensity of Budapest City

Abstract

AbstractThe vulnerability of fossil fuel prices to worldwide events such as the recent coronavirus (COVID‐19) pandemics increases the interest in renewable energy resources that offer more stable energy generation costs. Solar energy is one of the most abundant renewable energy resources that have gained significant interest in the last decades with various challenges related to the forecastingof the energy production from these systems. Solar radiation intensity varies due to the daily and seasonal changes in the sun's position in addition to the variation in the sky clearness from one location to another which is considered as an important factor that affects the deployment of solar energy systems. This study aims to develop statistical models—mainly regression models and parametric model based on ASHRAE model—to estimate the hourly diffuse radiation in Budapest as a case study using the measured hourly global and diffuse radiation between 2011 and 2018. The prediction models relate the clearness index (which is obtained from the extraterrestrial and global radiation) and the global radiation through a generalized equation. The parametric model was developed by finding the optimal site‐specific constants of ASHRAE model for Budapest using the measured data that minimize the root mean square error. In addition, this study presents a comparison between the results from the developed models and the models reported in the literature. The results indicate that all the developed regression models had close correlation coefficients (R2) where the linear, power, and exponential models had the largest R2 (.69). Finally, the linear model was evaluated on a dataset outside the test data range where the linear model was capable of predicting the diffuse radiation with much better R2 (.93).