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

Abstract

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%.