Photovoltaic output power prediction based on thermoelectric coupling digital twin model of inverter

During the operation of a photovoltaic inverter, an increase in temperature significantly affects the output power, efficiency, service life, and stability of the photovoltaic inverter. However, traditional evaluation methods based on physical experiments are time-consuming and costly, making it difficult to fully capture the dynamic performance under real conditions. This paper proposes a DT-LSTM (Digital Twin-Long Short-Term Memory) output power prediction method based on the thermoelectric coupling digital twin model of the inverter. Firstly, a digital twin model based on the equivalent circuit of the inverter is established; secondly, internal physical field data are calculated and parameter identification is performed; finally, the inverter output power is predicted and compared with traditional methods. The research results show that the prediction accuracy of the proposed method is improved, with the Mean Absolute Percentage Error (MAPE) optimized to 7.81%.