文献类型：期刊文献

英文题名：Development of a solar radiation measuring instrument for building energy management system

作者：Yang, Jie[1];Wang, Xiaotian[1];Li, Lin[2];Yuan, Keya[3]

第一作者：Yang, Jie

通讯作者：Li, L[1]

机构：[1]Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China;[2]Beijing Union Univ, Coll Appl Sci & Technol, Beijing 100101, Peoples R China;[3]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China

第一机构：Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Appl Sci & Technol, Beijing 100101, Peoples R China.|[1141775]北京联合大学应用科技学院;[11417]北京联合大学;

年份：2025

卷号：96

期号：5

外文期刊名：REVIEW OF SCIENTIFIC INSTRUMENTS

收录：;EI(收录号：20252218535585);Scopus(收录号：2-s2.0-105006480083);WOS:【SCI-EXPANDED(收录号:WOS:001491360000001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 42275143) and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant No. SJCX24_0442).

语种：英文

摘要：Excellent architectural design, energy-efficient control systems, and smart home technologies need to take into account the influence of solar radiation. Therefore, there is a necessity for high-precision measurement of solar radiation. However, existing solar radiation instruments are susceptible to environmental factors such as wind speed, air temperature, and air density, resulting in significant measurement errors. Therefore, this paper proposes the design of a solar radiation measurement instrument based on the thermoelectric effect. By integrating neural network algorithms, this instrument can mitigate the influence of environmental factors on solar radiation measurement. First, employing computational fluid dynamics (CFD) for multi-physics simulations of the instrument yielded solar radiation values under various environmental parameters. Subsequently, employing neural network algorithms to train and learn from the CFD simulation results, a quantitative relationship between solar radiation values and environmental parameters was established. This formed a radiation measurement error correction algorithm to mitigate the influence of environmental parameters on solar radiation observation results. Finally, constructing a radiation observation platform validated the measurement accuracy of the instrument. The experimental results indicate that the maximum radiation error of the new instrument is -3.97%, with an average radiation error of -0.16%, and the full-scale radiation error is less than 3.88%.