TY - CONF
T1 - Implementing IoT Technology for Energy Optimization in Rooftop Solar PV Plants: A Concentrated Validation Study
AU - Lara-Vargas, Fabian Alonso
AU - de la Ossa-Rivera, Jimena
AU - Jaramillo-Mira, Santiago
AU - Vargas-Salgado, Carlos
AU - Aguila-Leon, Jesus
AU - Villabon-Lopez, Evelyn
PY - 2025/5/1
Y1 - 2025/5/1
N2 - The present study presents a low-cost Internet of Things (IoT) monitoring system for rooftop solar photovoltaic systems to promote sustainable energy solutions. Using a Raspberry Pi Pico W and low-cost sensors, the system enables real-time monitoring of important variables such as solar panel temperature, solar radiation, inverter temperature, and indoor temperature. A case study conducted in Montería, Colombia, validated the accuracy of the proposed system by comparing its measurements with those of commercial meters using metrics such as Root Mean Square Error (RMSE), correlation, Mean Absolute Error (MAE), and R -Square (R2). The system achieved comparable correlation values between 0.84 and 0.98, RMSE between 74.6 and 1.2, MAE between 3.22 and 1.2, and R2 between 0.88 and 0.98. The results demonstrate the system’s ability to increase the performance of solar systems and thus support a sustainable energy transition. This study highlights the need for efficient monitoring and management of solar photovoltaic systems to maximize potential and minimize environmental impact. By examining precise and reliable monitoring methods, electronic prototypes for photovoltaic monitoring systems will be validated. These results highlight the importance of integrating IoT technology and data analytics to improve the automation, control, and monitoring of photovoltaic systems and promote renewable energy adoption and a sustainable future.
AB - The present study presents a low-cost Internet of Things (IoT) monitoring system for rooftop solar photovoltaic systems to promote sustainable energy solutions. Using a Raspberry Pi Pico W and low-cost sensors, the system enables real-time monitoring of important variables such as solar panel temperature, solar radiation, inverter temperature, and indoor temperature. A case study conducted in Montería, Colombia, validated the accuracy of the proposed system by comparing its measurements with those of commercial meters using metrics such as Root Mean Square Error (RMSE), correlation, Mean Absolute Error (MAE), and R -Square (R2). The system achieved comparable correlation values between 0.84 and 0.98, RMSE between 74.6 and 1.2, MAE between 3.22 and 1.2, and R2 between 0.88 and 0.98. The results demonstrate the system’s ability to increase the performance of solar systems and thus support a sustainable energy transition. This study highlights the need for efficient monitoring and management of solar photovoltaic systems to maximize potential and minimize environmental impact. By examining precise and reliable monitoring methods, electronic prototypes for photovoltaic monitoring systems will be validated. These results highlight the importance of integrating IoT technology and data analytics to improve the automation, control, and monitoring of photovoltaic systems and promote renewable energy adoption and a sustainable future.
U2 - 10.1007/978-3-031-88854-0_2
DO - 10.1007/978-3-031-88854-0_2
M3 - Ponencia publicada en las memorias del evento sin ISBN o ISSN
SP - 14
EP - 26
ER -