Comparative analysis of photovoltaic performance metrics for reliable performance loss rate

A reliable performance loss rate of photovoltaic systems requires accurate and reliable performance metrics. This study proposes a systematic method for assessing the performance metrics, particularly predicted power models in terms of both accuracy and uncertainty. The gist of the proposed method is to examine how accurately a predicted power model predicts the manipulated degradation in a controlled environment. For this, the proposed method divides a given dataset evenly into base data (to generate reference performance) and test data (to generate test performance via manipulation) so that the two data have similar features. The proposed method also utilizes the bootstrap iteration to derive a reliable assessment. The novelty of this study is that the proposed method estimates both the accuracy and uncertainty of arbitrary predicted power models, which is missing in existing works. Extensive experiments using the proposed method with real-world datasets reveal the followings. One interesting observation is that a well-known machine learning prediction model, not considered in existing works, exhibits the best performance in terms of both accuracy and uncertainty. Existing predicted power models require different experiment settings to produce reliable performance. The number of test data is closely related to uncertainty, but not much related to accuracy.