There are several time-scales involved in performance variations of photovoltaic devices. In the short term, there is the direct influence of the environment as discussed in Section 4. In the mid-term there is the Staebler–Wronski effect for a-Si [48,49] or pre-conditioning for CIGS, which affects the annual energy prediction by less than 5% typically and are not further explored here. The effect for CIGS is slightly contradictory as there is no clarity of the time scales involved yet [4,50]. There are also long-term gradual degradation effects which affect all device technologies [51]. In many published discussions there are generalisations of technologies. It is shown below that such a general behaviour was not observed at CREST. The maximum power point data of all devices within an irradiance range of 650–750 W/m2 have been extracted and corrected to 700 W/m2 and 25 °C using bi-linear interpolation. These power values are annotated as P700. This particular irradiance level was chosen as there is a statistically significant number of data points in each month of the year and thus fewer outliers to affect the analysis. The result is shown in Fig. 9 (top graph). To contrast power and energy, the monthly performance ratios are also plotted (bottom graph)Table 2.
annual energy prediction
less than 5%
%
devices
irradiance range
650–750 W/m2
W/m2
devices
irradiance
700 W/m2
W/m2
devices
25 °C
°C