A critical review of the models used to estimate solar radiation

TLDR: In this paper, the authors present a critical literature review of solar radiation estimation models and provide a detailed analysis on available models, including Artificial Neural Networks (ANNs) and Sunburst Networks (SNNs).

Abstract: Solar radiation data is critical to the design and operation of solar energy utilization systems, so a large number of models have been proposed and developed to estimate solar radiation in the past ten years. However, the performances of such models are controversial in different studies, and there is a lack of systematic comparison among them. In addition, few studies pay attention to the time scales and practicability of the models. This paper focuses on solving these questions through a critical literature review and the authors believe it can benefit researchers to perform further investigations about solar radiation. This paper reviews and compares the models from the points of view of time scale and estimation type for the first time. Furthermore, a large amount of data about the evaluation metrics (root mean square error and mean absolute percentage error) from different studies is summarized to clarify the performances of proposed models. The questions arising from the processing of source data are also carefully examined. This paper has presented a novel method to compare the estimation models and has provided a detailed analysis on available models. The results indicate that the sunshine duration fraction models and artificial neural networks have similar performances when used to estimate monthly average daily global radiation and daily global radiation, while more work is needed to study the estimation method on smaller time intervals and the mechanisms of atmospheric attenuation for solar radiation.