A climate zone approach to global solar radiation modelling using artificial neural networks

Abstract

Abstract Information on solar availability is crucial in the study of both passive building designs and active solar energy systems. There are still many locations within different climate zones worldwide that do not have solar radiation measurements. Correlation between solar radiation and the more commonly measured meteorological variables such as temperature and sunshine hours is useful for locations with no measured solar radiation data. This is also useful for locations with measured solar radiation data, in that any missing data due to equipment breakdown or malfunction can be modelled. Global solar radiation (GSR) was modelled for 96 cities in different climate zones across China using artificial neural networks (ANNs). The novelty of this study is the climate zone approach, by which locations with similar climates were modelled together. Climate classification was based on both the traditional thermal climates and the solar climates. Two sets of models were developed based on measured diurnal temperatures and sunshine hours. Model performance in terms of the predictive power of ANN solar radiation models was evaluated through the Nash-Sutcliffe efficiency coefficient (NSEC). Error analysis of the predicted solar radiation as compared with the measured data was also conducted for each of the 96 cities.