Published data on photovoltaic (PV) degradation measurements were aggregated and re‐examined. The subject has seen an increased interest in recent years resulting in more than 11 000 degradation rates in almost 200 studies from 40 different countries. As studies have grown in number and size, we found an impact from sampling bias attributable to size and accuracy. Because of the correlational nature of this study we examined the data in several ways to minimize this bias. We found median degradation for x‐Si technologies in the 0.5–0.6%/year range with the mean in the 0.8–0.9%/year range. Hetero‐interface technology (HIT) and microcrystalline silicon (µc‐Si) technologies, although not as plentiful, exhibit degradation around 1%/year and resemble thin‐film products more closely than x‐Si. Several studies showing low degradation for copper indium gallium selenide (CIGS) have emerged. Higher degradation for cadmium telluride (CdTe) has been reported, but these findings could reflect a convolution of less accurate studies and longer stabilization periods for some products. Significant deviations for beginning‐of‐life measurements with respect to nameplate rating have been documented over the last 35 years. Therefore, degradation rates that use nameplate rating as reference may be significantly impacted. Studies that used nameplate rating as reference but used solar simulators showed less variation than similar studies using outdoor measurements, even when accounting for different climates. This could be associated with confounding effects of measurement uncertainty and soiling that take place outdoors. Hotter climates and mounting configurations that lead to sustained higher temperatures may lead to higher degradation in some, but not all, products. Wear‐out non‐linearities for the worst performing modules have been documented in a few select studies that took multiple measurements of an ensemble of modules during the lifetime of the system. However, the majority of these modules exhibit a fairly linear decline. Modeling these non‐linearities, whether they occur at the beginning‐of‐life or end‐of‐life in the PV life cycle, has an important impact on the levelized cost of energy. Copyright © 2016 John Wiley & Sons, Ltd.

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Documentation of the energy yield of a large photovoltaic (PV) system over a substantial period can be useful to measure a performance guarantee, as an assessment of the health of the system, for verification of a performance model to then be applied to a new system, or for a variety of other purposes. Although the measurement of this performance metric might appear to be straightforward, there are a number of subtleties associated with variations in weather and imperfect data collection that complicate the determination and data analysis. A performance assessment is most valuable when it is completed with a very low uncertainty and when the subtleties are systematically addressed, yet currently no standard exists to guide this process.This report summarizes a draft methodology for an Energy Performance Evaluation Method, the philosophy behind the draft method, and the lessons that were learned by implementing the method. The general philosophy behind the methodology includes the following features:• The method is performance-model agnostic.• The performance model must not be inadvertently modified, when being implemented on the measured meteorological data sets, relative to the model that was used on the historical data set.

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