Temperature Prediction for Photovoltaic Inverters Using Particle Swarm Optimization-Based Symbolic Regression: A Comparative Study

Accurate temperature modeling is crucial for main taining the efficiency and reliability of solar inverters. This paper presents an innovative application of symbolic regression based on particle swarm optimization (PSO) for predicting the temperature of photovoltaic inverters, offering a novel approach that balances accuracy and computational efficiency. The study evaluates the performance of a PSO-based symbolic regression model compared to multiple linear regression (MLR) and a symbolic regression model based on genetic algorithms (GA). The models were developed using a dataset that included inverter temperature, active power, and DC bus voltage, collected over a year in hourly intervals from a rooftop photovoltaic system in a tropical region. The dataset was divided, with 70% used for training and the remaining 30% for testing. The symbolic regres sion model based on PSO demonstrated superior performance, achieving lower values of the root mean square error (RMSE) and mean absolute error (MAE) of 3.97 and 3.31, respectively. Furthermore, the PSO-based model effectively captured the nonlinear relationships between variables, outperforming the MLR model. It also exhibited greater computational efficiency, requiring fewer iterations than traditional symbolic regression approaches. These findings open new possibilities for real-time monitoring of photovoltaic inverters and suggest future research directions, such as generalizing the PSO model to different environmental conditions and inverter types.