Simplified Method for Predicting Hourly Global Solar Radiation Using Extraterrestrial Radiation and Limited Weather Forecast Parameters
Author:
Yang Xinyu12, Ji Ying12, Wang Xiaoxia12, Niu Menghan12, Long Shuijing3, Xie Jingchao12, Sun Yuying12
Affiliation:
1. Beijing Key Laboratory of Green Building Environment and Energy Saving Technology, Beijing University of Technology, Beijing 100124, China 2. Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China 3. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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/m2. 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/m2. 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.
Funder
Youth Program of the National Natural Science Foundation of China
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference35 articles.
1. IEA (2021, June 08). Tracking Buildings 2020. Paris, Available online: https://wwwieaorg/reports/tracking-buildings-2020. 2. Europe Environment Agency (2021, December 03). Greenhouse Gas Emissions from Energy Use in Buildings in Europe. 26 October 2020, Available online: https://www.eea.europa.eu/ims/greenhouse-gas-emissions-from-energy. 3. U.S. Energy Information Administration (2021, December 08). U.S. Energy-Related Carbon Dioxide Emissions, 2019, Available online: https://www.eia.gov/environment/emissions/carbon/archive/2020/. 4. Building Energy Research Center, Tsinghua University (2021). 2021 Annual Report on China Building Energy Efficiency, China Architecture Publishing & Media Co., Ltd. 5. Wang, S.-Y., Qiu, J., and Li, F.-F. (2018). Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records. Energies, 11.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|