Development of a solar radiation measuring instrument for building energy management system.

This article focuses on the design and evaluation of a novel solar radiation measuring instrument based on the thermoelectric effect, enhanced by a neural network algorithm to correct measurement errors caused by environmental factors such as wind speed, air temperature, solar radiation angle, and air density. Using computational fluid dynamics (CFD) simulations, the study established a quantitative relationship between solar radiation values and environmental parameters, which was then applied to improve measurement accuracy. Experimental validation against a Kipp & Zonen CMP10 pyranometer demonstrated that the new instrument achieves a maximum radiation error of −3.97%, an average error of −0.16%, and a full-scale error below 3.88%. The instrument’s high precision and environmental adaptability suggest its potential utility in climate research, solar energy management, architectural design, and meteorological applications requiring reliable solar radiation data.