Abstract
The present work aimed to develop an autonomous instrument for indirectly measuring solar irradiance from silicon sensors. Through laboratory tests, using a precision digital solar power meter device (SM206-SOLAR), with an error range of ±10% per reading and ±0.38% W/m2 per °C, and photovoltaic trainer equipment (Lucas Nulle), a statistical correlation model was developed between the short-circuit current of the NPA5S-12H solar module and the measured artificial irradiance. A significant linear correlation was determined with a mean absolute percentage error (MAPE) of 6.97%, an average root-mean-square error (RMSE) of ±26 W/m2 per °C and a relative percentage root mean square error (rRMSE) of 10.76%. For real-time data collection purposes, a data-logger device with wireless communication functions based on a Wi-Fi link and IoT MQTT protocol was developed. Finally, a SQL server and web application were configured on a public Linux server to collect and present data in real-time.
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE Green Technologies Conference, GreenTech 2023 |
| Publisher | IEEE Computer Society |
| Pages | 40-45 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665492874 |
| DOIs | |
| State | Published - 2023 |
| Event | 15th Annual IEEE Green Technologies Conference, GreenTech 2023 - Denver, United States Duration: 19 Apr 2023 → 21 Apr 2023 |
Publication series
| Name | 2023 IEEE Green Technologies Conference (GreenTech) |
|---|
Conference
| Conference | 15th Annual IEEE Green Technologies Conference, GreenTech 2023 |
|---|---|
| Country/Territory | United States |
| City | Denver |
| Period | 19/04/23 → 21/04/23 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- PV data-logger
- correlational model
- solar cells
- solar irradiance estimation
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