Review of empirical solar radiation models for estimating global solar radiation of various climate zones of China/ (Record no. 14906)
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| fixed length control field | 02431nab a2200217 4500 |
| 005 - DATE & TIME | |
| control field | 20231010113518.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231010b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Shaban G Gouda |
| 245 ## - TITLE STATEMENT | |
| Title | Review of empirical solar radiation models for estimating global solar radiation of various climate zones of China/ |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Sgae, |
| Date of publication, distribution, etc | 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | Vol 44, issue 2, 2020 : ( 168–188 p.). |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Utilizing solar energy requires accurate information about global solar radiation (GSR), which is critical for designers and manufacturers of solar energy systems and equipment. This study aims to examine the literature gaps by evaluating recent predictive models and categorizing them into various groups depending on the input parameters, and comprehensively collect the methods for classifying China into solar zones. The selected groups of models include those that use sunshine duration, temperature, dew-point temperature, precipitation, fog, cloud cover, day of the year, and different meteorological parameters (complex models). 220 empirical models are analyzed for estimating the GSR on a horizontal surface in China. Additionally, the most accurate models from the literature are summarized for 115 locations in China and are distributed into the above categories with the corresponding solar zone; the ideal models from each category and each solar zone are identified. Comments on two important temperature-based models that are presented in this work can help the researchers and readers to be unconfused when reading the literature of these models and cite them in a correct method in future studies. Machine learning techniques exhibit performance GSR estimation better than empirical models; however, the computational cost and complexity should be considered at choosing and applying these techniques. The models and model categories in this study, according to the key input parameters at the corresponding location and solar zone, are helpful to researchers as well as to designers and engineers of solar energy systems and equipment.<br/> |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Hussein, Zakia |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Luo, Shuai |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Yuan, Qiaoxia |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 12665 |
| Host Itemnumber | 17140 |
| Place, publisher, and date of publication | London: Sage Publication Ltd, 2019. |
| Title | Progress in Physical Geography: Earth and Environment/ |
| International Standard Serial Number | 03091333 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1177/0309133319867213 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | E-Journal |
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