STC Short-Circuit Current Prediction and I-V Simulation of Colored BIPV Modules With Machine Learning and One-Diode Equivalent Circuit Models

Colored photovoltaic (PV) modules offer improved aesthetics at the cost of electrical performance loss. Here, we demonstrate a hybrid approach combining experiments, machine learning, and equivalent-circuit model to predict and simulate I-V parameters of colored crystalline-silicon PV modules. First, to predict the short-circuit current for different colors and opacity levels, three models-multiple linear regression (MLR), optimized support vector regression (SVR), and optimized Gaussian pro-cess regression (GPR)-are trained and evaluated with a ten-fold cross validation. The MLR model shows an MAE = 3.58 and an RMSE = 5.17. The accuracy could be further improved with the more advanced models, i.e., an optimized SVR (MAE = 0.22, RMSE = 0.24) or an optimized GPR (MAE = 0.13, RMSE = 0.17). Following, by taking the predicted short-circuit current values as inputs into a one-diode equivalent circuit model, I-V curves of two multicolored modules are simulated; and information such as module power P-mpp and current mismatch loss are extracted. There is a twofold advantage of implementing our approach: first, it serves as an efficient design space exploration methodology for a wide parameter space (e.g., colors), i.e., evaluating the PV performance for new colors without samples fabrication, thus saving time and resources; and second, it guides architects, designs, and engineers in color/design selection to achieve a balance between aesthetics and engineering considerations.

Automated summary

This study demonstrates a hybrid approach combining experiments, machine learning, and equivalent-circuit model to predict and simulate the performance of colored photovoltaic (PV) modules. By training multiple models and using the predicted values as inputs, the researchers are able to accurately predict the short-circuit current of PV modules with different colors and opacity levels, and perform circuit simulations and performance evaluations.