Improving the Irradiance Data Measured by Silicon-Based Sensors

Abstract

Silicon-based sensors are widely used for monitoring solar irradiance, in particular, in the field of Photovoltaic (PV) applications. We present a method to correct the global horizontal irradiance measured by silicon-based sensors that reduces the difference to the standard thermopile sensor measurements. A major motivation to use silicon-based sensors for the measurements of irradiance is their lower cost. In addition, their response time is much lower, and their spectral response is much closer to that of the PV systems. The analysis of the differences is based on evaluating four parameters that influence the sensor measurements, namely the temperature, cosine error, spectral mismatch, and calibration factor. Based on the analysis, a correction model is applied to the silicon sensors measurements. The model separates measurements under a clear sky and cloudy sky by combining the clearness index and the solar zenith angle. By applying the correction model on the measurements of the silicon-based sensor, the differences between sensor readings have been reduced significantly. The relative root mean squared difference (rRMSD) between the daily solar irradiation measured by both sensors decreased from 10.6% to 5.4% after applying the correction model, while relative mean absolute difference (rMAD) decreased from 7.4% to 2.5%. The difference in total annual irradiation decreased from 70 KWh/m2 (6.5%) to 15 kWh/m2 (1.5%) by the correction. The presented correction method shows promising results for a further improvement in the accuracy of silicon-based sensors.

RETROFITTING OUTDATED PYRANOMETER FOR IoT INTEGRATION TOWARDS A CIRCULAR ECONOMY MODEL

“…Table 3. summarizes the calculus results of ( 9), ( 10), (11), and (12) relations for the two data sets. From Table 3 it can seen, that the calculated percentage value of NRMSE is almost negligible, as well as the calculated percentage value of Pearson's correlation coefficient, which has a positive matching correlation in the same direction toward unity.…”

“…Finally, further research directives can include the merging of technologies between nowcasting and forecasting online weather data servers, and locally, deployable, IoT weather stations, for better results. Also, further calibration and accuracy improvements can be implemented on the software side to eliminate data disruptions according to studies revealed by Azouzoute et al [10] and Mubarak et al [11].…”