A novel adaptive approach for improvement in the estimation of hourly diffuse solar radiation: A case study of China

Abstract

Liu & Jordan–type models are still widely used to estimate diffuse radiation in various regions because of their simplicity. This paper proposes an adaptive approach of combining the grouping of kt–k data points with Liu & Jordan–type models to estimate the hourly diffuse radiation more accurately. This study was conducted using hourly radiation data measured in three regions in China (Shanghai, Xi'an, and Lhasa). The main novelties reported herein are the introduction of α as a constraint indicator and the proposal of a criterion for grouping kt–k data points. Grouping models, which adopted the functional form of cubic polynomials, were established in three regions based on the grouped data. Compared with the all-data model results, the percentage of mean absolute bias error (MABE%) and relative root mean square error (RMSE%) for the grouping model decreased by approximately 4% in Shanghai and Xi'an and 6% in Lhasa. Furthermore, weather condition analysis showed that the grouping models had the best adaptability on overcast days, followed by cloudy days and sunny days. A similar analysis was performed for solar time, with the best adaptability occurring at sunrise and sunset and the worst occurring at 9 am. The results also suggest that similar working procedures could contribute to hourly diffuse radiation estimation in other fields.