Simplified Method for Predicting Hourly Global Solar Radiation Using Extraterrestrial Radiation and Limited Weather Forecast Parameters

Xinyu Yang, Ying Ji (), Xiaoxia Wang, Menghan Niu, Shuijing Long, Jingchao Xie and Yuying Sun
Additional contact information
Xinyu Yang: Beijing Key Laboratory of Green Building Environment and Energy Saving Technology, Beijing University of Technology, Beijing 100124, China
Ying Ji: Beijing Key Laboratory of Green Building Environment and Energy Saving Technology, Beijing University of Technology, Beijing 100124, China
Xiaoxia Wang: Beijing Key Laboratory of Green Building Environment and Energy Saving Technology, Beijing University of Technology, Beijing 100124, China
Menghan Niu: Beijing Key Laboratory of Green Building Environment and Energy Saving Technology, Beijing University of Technology, Beijing 100124, China
Shuijing Long: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Jingchao Xie: Beijing Key Laboratory of Green Building Environment and Energy Saving Technology, Beijing University of Technology, Beijing 100124, China
Yuying Sun: Beijing Key Laboratory of Green Building Environment and Energy Saving Technology, Beijing University of Technology, Beijing 100124, China

Energies, 2023, vol. 16, issue 7, 1-16

Abstract: Solar radiation has important impacts on buildings such as for cooling/heating load forecasting, energy consumption forecasting, and multi-energy complementary optimization. Two types of solar radiation data are commonly used in buildings: radiation data in typical meteorological years and measured radiation data from meteorological stations, both of which are types of historical data. However, it is difficult to predict the hourly global solar radiation, which affects the application of relevant prediction models in practical engineering. Most existing methods for predicting hourly global solar radiation have issues such as difficulty in obtaining input parameters or complex data processing, which limits their practical engineering applications. This study proposed a simplified method to accurately predict the hourly horizontal solar radiation using extraterrestrial solar radiation, weather types, cloud cover, air temperature, relative humidity, and time as the input parameters. The back-propagation network, support vector machine, and light gradient boosting machine (LightGBM) models were used to establish the prediction model, and Shapley additive explanations were used to analyze the relationship between the input variables and the prediction results to simplify the structure of the prediction model. Taking Lanzhou New District in Gansu Province as an example, the results showed that the LightGBM model performed the best, with the root mean square error of 126.1 W/m 2 . Shapley additive explanations analysis showed that weather type was not a significant factor in the LightGBM model. Therefore, the weather type was removed from the LightGBM model and the root mean square error was 135.2 W/m 2 . The results showed that extra-terrestrial radiation and limited weather forecast parameters can be used to predict hourly global solar radiation with satisfactory prediction results.

Keywords: hourly global solar radiation; simplified prediction method; extraterrestrial solar radiation; LightGBM; SHAP analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/7/3215/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/7/3215/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:7:p:3215-:d:1114586

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().