Development of the estimation model for the maximum power point of building-applied photovoltaic systems based on machine learning

With increased photovoltaic (PV) applications in buildings, operational and maintenance (O & M) is crucial to preserving initial performance. Faults detection and diagnostics (FDD) methods, such as current and voltage measurement, are used to identify system errors by comparing measured values with calculated maximum power point (MPP) outputs. This method typically uses the One -Diode model to calculate MPP values and considers them as optimal performance outputs. However, the accuracy can inevitably vary based on PV installation conditions and types of solar cells. Data-driven models have been developed to address these complexities but require further exploration for practical building applications. This study developed a data-driven model using an artificial neural network (ANN) to estimate MPP in the context of FDD for various building -applied PV systems. Initial training was conducted with hourly-based simulated data, and the model was validated using real-world building-applied PV system data, including building -attached PV (BAPV) and building-integrated PV (BIPV) types. To improve the performance of the initial MPP estimation model, additional training and re-validation processes were conducted using the measured data of the BIPV system. Initial results showed acceptable performance for power, current, and voltage estimation with less than 30% CvRMSE for building-attached PV systems. Building-integrated PV systems, affected by external factors like shading, yielded less accuracy with over 30% CvRMSE. After additional training, the model showed improved per-formance, including around 11% CvRMSE for all parameters. This study indicates that the ANN -based MPP estimation model can be practically applied to FDD with acceptable accuracy.

Automated summary

A data-driven model using an artificial neural network (ANN) was developed to estimate the maximum power point (MPP) of building-applied PV systems and validated using real-world data. The study demonstrates that the model can be practically applied to fault detection and diagnostics (FDD) with acceptable accuracy.