Inference of missing data in photovoltaic monitoring datasets

Abstract

Photovoltaic (PV) systems are frequently covered by performance guarantees, which are often based on attaining a certain performance ratio (PR). Climatic and electrical data are collected on site to verify that these guarantees are met or that the systems are working well. However, in-field data acquisition commonly suffers from data loss, sometimes for prolonged periods of time, making this assessment impossible or at the very best introducing significant uncertainties. This study presents a method to mitigate this issue based on back-filling missing data. Typical cases of data loss are considered and a method to infer this is presented and validated. Synthetic performance data is generated based on interpolated environmental data and a trained empirical electrical model. A case study is subsequently used to validate the method. Accuracy of the approach is examined by creating artificial data loss in two closely monitored PV modules. A missing month of energy readings has been replenished, reproducing PR with an average daily and monthly mean bias error of about -1 and -0.02%, respectively, for a crystalline silicon module. The PR is a key property which is required for the warranty verification, and the proposed method yields reliable results in order to achieve this.