Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in‐field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification of the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.

Knowledge of roof geometry and physical features is essential for evaluation of the impact of multiple rooftop solar photovoltaic (PV) system installations on local electricity networks. The paper starts by listing current methods used and stating their strengths and weaknesses. No current method is capable of delivering accurate results with publicly available input data. Hence a different approach is developed, based on slope and aspect using aircraft-based Light Detection and Ranging (LiDAR) data, building footprint data, GIS (Geographical Information Systems) tools, and aerial photographs. It assesses each roof’s suitability for PV deployment. That is, the characteristics of each roof are examined for fitting of at least a minimum size solar power system. In this way the minimum potential solar yield for region or city may be obtained. Accuracy is determined by ground-truthing against a database of 886 household systems. This is the largest validation of a rooftop assessment method to date. The method is flexible with few prior assumptions. It can generate data for various PV scenarios and future analyses.

Due to the overall declining costs of photovoltaic systems, market players in the operation and maintenance sector are under increasing price pressure when offering their services. The automation and standardization of maintenance and failure tickets as well as their statistical and economical evaluation are key to ensure optimal yield and long lifetime. A thorough understanding of typical faults, classified through a standardized taxonomy, can be a pathway of developing location and technology specific decision support, offering cost‐time efficient solutions to reduce component downtime and costs in case of failure appearance. A useful method for this approach is the Cost Priority Number, a methodology to assess technical failures and their economic impact in energy systems. In this work, this method is further improved to be applied to individual use cases to make it useful in the actual operation of PV plants. A standardized ticket taxonomy for the operational phase of PV systems has been developed where more than 35,000 PV systems' tickets have been statistically evaluated, and a fully automated methodology to calculate the cost of individual maintenance tickets has been developed.