Experimental Evaluation of Solar Radiation and Solar Efficacy Models and Performance of Data-Driven Models

Abstract

Weather data are major input for building energy usage predictions. However, solar-radiation-related historical and real-time weather data are unavailable or incomplete in many locations. Therefore, many models, which use more available weather parameters to predict solar radiation’s components, were developed in the last 30 years. An experimental evaluation of these models is needed since measurement devices and satellite techniques are improved, and weather files are consequently updated. In this study, we review, calibrate, and validate the accuracy of global and diffuse irradiation prediction models and efficacy models using experimental data over an 18-month data collection period in Taipei, Taiwan, and Kingsville, Texas. The evaluation also covers data-driven models such as neural networks. The results show that the SUNY model provides good solar irradiance estimations; Perez and Muneer efficacy models provide good daylight illuminance estimations; and Erbs, Muneer, Reindl, and Perez models have similar accuracy but different error trends when separating direct and diffuse irradiance. The results can be used as a guideline when filling in solar-radiation-related fields.