A critical review of the models used to estimate solar radiation

Zhang, Jianyuan; Zhao, Li; Deng, Shuai; Xu, Weicong; Zhang, Ying

doi:10.1016/j.rser.2016.11.124

Cited by 223 publications

(127 citation statements)

References 89 publications

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“…Many approaches have been developed to estimate surface solar radiation accurately (Khatib et al, ; Liu et al, ; Olatomiwa et al, ; Sun et al, ; Wang et al, ; Zhang et al, ), which can be generally categorized as follows: Estimation by semiempirical and semiphysical formulae with conventional meteorological parameters (e.g., sunshine hour, cloud, temperature, and humidity) as inputs (Angstrom, ; Davies et al, ; Thornton & Running, ; Bakirci, ; Besharat et al, ; Hassan et al, ; Liu et al, ). A simple empirical model can be established with its coefficients varying with time and space, which need to be calibrated using long‐term radiation observation data in certain areas. Estimation by the artificial neural network method with meteorological observations (Jiang, ; Linares‐Rodriguez et al, ; Tang et al, ; Ramedani et al, ; Kashyap et al, ; Wang et al, ).…”

Section: Discussionmentioning

confidence: 99%

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

Zeng

Wang

Gui

et al. 2020

Earth and Space Science

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Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

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Section: Discussionmentioning

confidence: 99%

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

Zeng

Wang

Gui

et al. 2020

Earth and Space Science

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“…An empirical model correlates solar radiation with other easily measurable parameters, such as sunshine duration, temperature and humidity, by applying concise mathematical functions. Due to its simplicity and strong operability, the empirical model is much more convenient for engineering applications [10]. The first Sunshine duration model was proposed by Angstrom [11], which related the clear sky index with the sunshine duration fraction by using the linear equation.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Solar Radiation Model Using Measured Data in a Specific Region

Yanıktepe¹,

Kara²,

Özalp³

2017

RAD Conference Proceedings

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“…However, Liu et al suggested using the B-C model after comparing various empirical models with measured data from 15 meteorological stations in Northern China due to better accuracy and ease of determining the empirical factors [30]. It should be noted that the estimation error for models based on ambient temperature is larger than for those based on sunshine duration [22].…”

Section: Estimation Models For Daily Global Solar Radiationmentioning

confidence: 99%

“…The artificial intelligence approach has been utilized by some researchers to predict the global solar radiation [22,[34][35][36][37]. However, more factors are involved in the artificial intelligence techniques compared to empirical models.…”

Section: Artificial Intelligence Approachmentioning

confidence: 99%

Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections

et al. 2018

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Solar radiation is one of the most important factors influencing the temperature distribution on bridge girder cross-sections. The bridge temperature distribution can be estimated using estimation models that incorporate solar radiation data; however, such data could be cost-or time-prohibitive to obtain. A review of literature was carried out on estimation models for solar radiation parameters, including the global solar radiation, beam solar radiation and diffuse solar radiation. Solar radiation data from eight cities in Fujian Province in southeastern China were obtained on site. Solar radiation models applicable to Fujian, China were proposed and verified using the measured data. The linear Ångström-Page model (based on sunshine duration) can be used to estimate the daily global solar radiation. The Collares-Pereira and Rabl model and the Hottel model can be used to estimate the hourly global solar radiation and the beam solar radiation, respectively. Three bridges were chosen as case study, for which the temperature distribution on girder cross-sections were monitored on site. Finite element models (FEM) of cross-sections of bridge girders were implemented using the Midas program. The temperature-time curves obtained from FEM showed very close agreement with the measured values for summertime. Ignoring the solar radiation effect would result in lower and delayed temperature peaks. However, the influence of solar radiation on the temperature distribution in winter is negligible.

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A critical review of the models used to estimate solar radiation

Cited by 223 publications

References 89 publications

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

Estimation of Solar Radiation Model Using Measured Data in a Specific Region

Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections

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