Efficiency Measurement of Energy Yield from Solar Photovoltaic Cell with Embedded System of Alternative Clamp Meter

This paper presents a data analysis of energy production by photovoltaic systems at Prince of Songkla University, Surat Thani Campus, with a focus on the installation of a solar cell system on the campus roof. The energy generated by this system is transmitted to the main electrical control cabinet of the building. However, in case of a malfunction in the solar cell set, inspecting and identifying the cause of the abnormality can be challenging and time-consuming. Therefore, the use of current measuring devices is necessary to assess the operation of each inverter set in the photovoltaic system, facilitating the identification and prediction of abnormalities within the solar cell set. To achieve this, we conducted a linear regression analysis employing a machine learning model. Our findings indicate that the Mean Absolute Error (MAE) model is the most suitable machine learning model for determining the efficiency of the photovoltaic system in terms of current sensing, with analysis results of Phase A = 0.8414, Phase B = 1.0128, and Phase C = 0.8129. Additionally, for voltage sensing, the most appropriate model is the Mean Absolute Percentage Error (MAPE), with an analysis result of Phase A = 0.4857, Phase B = 0.5057, and Phase C = 0.4571. These results provide valuable insights for maintenance purposes and contribute to enhancing the overall functionality of the photovoltaic system.