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. 2022 Sep 20;22(19):7107.
doi: 10.3390/s22197107.

A Low-Cost Luxometer Benchmark for Solar Illuminance Measurement System Based on the Internet of Things

Omar Guillán Lorenzo  1 , Andrés Suárez-García  2 , David González Peña  1 , Manuel García Fuente  1 , Diego Granados-López  1
Affiliations

A Low-Cost Luxometer Benchmark for Solar Illuminance Measurement System Based on the Internet of Things

Omar Guillán Lorenzo et al. Sensors (Basel). .

Abstract

Natural illumination has an important place in home automation applications. Among other advantages, it contributes to better visual health, energy savings, and lower CO2 emissions. Therefore, it is important to measure illuminance in the most accurate and cost-effective way. This work compares several low-cost commercial sensors (VEML 7700, TSL2591, and OPT3001) with a professional one (ML-020S-O), all of them installed outdoors. In addition, a platform based on the Internet of Things technology was designed and deployed as a centralized point of data collection and processing. Summer months have been chosen for the comparison. This is the most adverse situation for low-cost sensors since they are designed for indoor use, and their operating range is lower than the maximum reached by sunlight. The solar illuminance was recorded every minute. As expected, the obtained bias depends on the solar height. This can reach 60% in the worst circumstances, although most of the time, its value stays below 40%. The positive side lies in the good precision of the recordings. This systematic deviation makes it susceptible to mathematical correction. Therefore, the incorporation of more sensors and data that can help the global improvement of the precision and accuracy of this low-cost system is left as a future line of improvement.

Keywords: Internet of Things (IoT); low-cost sensor; luxometer; natural illumination.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow diagram.
Figure 2
Figure 2
Spectral response of human eye and selected sensors.
Figure 3
Figure 3
The relative error in the responsivity between low-cost sensors and the human eye.
Figure 4
Figure 4
High-level circuit schematic of the PCB sensor board.
Figure 5
Figure 5
Block diagram of the end-to-end datapath.
Figure 6
Figure 6
The high-level flowchart of the program running in the microcontroller.
Figure 7
Figure 7
Detailed diagram of the containers’ infrastructure.
Figure 8
Figure 8
Experimental facility location at the High Polytechnic School of University of Burgos, Spain.
Figure 9
Figure 9
Illuminance measurements for 26 June 2022.
Figure 10
Figure 10
Illuminance low-cost sensor measurements versus professional one.
Figure 11
Figure 11
Histogram of the relative errors.
Figure 12
Figure 12
Relative errors grouped by solar altitude.
Figure 13
Figure 13
Correlation between low-cost sensors and professional one.
See this image and copyright information in PMC

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