ENVIRONMENTAL INFLUENCE MONITORING ON A HYBRID PHOTOVOLTAIC-THERMOELECTRIC GENERATION UTILIZING THE INTERNET OF THINGS

Abstract

Renewable energy, such as a hybrid photovoltaic-thermoelectric (PVTE) power generation, reduces dependence on conventional energy sources and gas emissions. This generation is installed outdoors, so various environmental factors continuously and simultaneously influence its performance. Therefore, these continuous and simultaneous effects on PVTE power generation should be accurately investigated through data acquisition employing the Internet of Things (IoT) and corresponding sensors. The electrical parameters employed PZEM-016 and PZEM-017 sensors, whereas the environmental parameters employed SHT30-FS200, I2C-IP68, Guva-S12SD, I2C air quality PM, and I2C-UART tipping bucket sensors for temperature and humidity, illuminance, ultraviolet (UV) radiation, pollution, and rainfall sensing, respectively. Meanwhile, AM2301 devices sensed photovoltaic (PV) and thermoelectric (TE) module temperatures. Principal component analysis (PCA), box plot, and correlation coefficient were employed for data analysis. The two highest environmental parameters that influence the PV, hybrid, and MPPT (Maximum Power Point Tracking) powers were UV radiation and illuminance, with a high correlation range between 0.820 and 0.863. While the TE power was moderately influenced by the temperature difference, with a correlation coefficient range of 0.511 - 0.542. The power-increasing rates due to illuminance were 2.61 watts/klux and 0.01 watts/klux for the PV and TE modules, respectively. The power ratios were 99.40% and 78.04%, and the energy ratios were 99.52% and 83.29% for the hybrid point and MPPT, respectively.