## 5 Conclusions  
Two performance matrices of fifteen PV systems of six module technologies operating under real conditions in three climates were calculated. The PV systems consist of one amorphous silicon (a-Si) solar cell system, one heterojunction with intrinsic thin layer (HIT) hybrid silicon system, one copper indium gallium selenide (CIGS) system, three monocrystalline silicon (mono-Si) systems, three cadmium telluride (CdTe) systems, and six polycrystalline silicon (p-Si) systems. The PV systems are located in Australia, Indonesia, and Italy. The matrices include performance ratio (PR) and performance loss rate (PLR). PLR was calculated using two different approaches. Seasonal and trend decomposition using locally weighted scatterplot smoothing (STL decomposition) was applied to temperature-corrected PR time-series, while the Year-on-Year approach was applied to renormalized energy values based on the power predictive model PVWatts. The latter was furthermore applied to sensor-based irradiance data as well as clear-sky modeled data. The general conclusions of this work are  
1. The annual-averaged temperature-corrected performance ratio, PR\({}_{\text{ann.}}\), with an average value of all systems from each technology. The CIGS system performed best with an average PR value of \(0.88\pm 0.04\). The least performing technology was the a-Si PV systems, with an average PR value of \(0.78\pm 0.05\). The p-Si systems in climate Cfb of Italy had a higher average PR of 0.84 than those operating in climates BWh (Australia) and Af (Indonesia), with the same value of 0.81.
2. Performance loss rates based on the STL approach. For almost all systems, the use of STL for the calculation of PLR is helpful, especially if monitoring data of high quality was not available. The p-Si systems show the lowest PLR among the technologies with an average PLR value of \(-0.6\%\)/year. The strongest performance loss was experienced by a-Si modules at \(-1.58\%\)/year.  
Results from Indonesia were appreciated because they provide the first study on the topic. However, they were based on only two PV systems with a data period of two and three years, which is not representative enough to draw a strong general conclusion and is subject to very high uncertainties. In addition, PV systems that used a-Si, CIGS, and HIT were represented by only one PV system each. More studies involving PV systems in South-East Asia and other climates are needed. Moreover, further investigations are required to show the nonlinear behavior in PV system performance, which is the actual case, while this work and many others assume a linear performance.  
The differences in the calculation methods, in this case using STL and YoY, result in significant degradation values. STL employs a decomposition of the temperature corrected PR and the YoY was based on differences in daily instances of renormalized energy values. Distinct differences among the calculated PLR values between both approaches demand a standardization along the whole calculation chain from input data treatment to the application of statistical methods to calculate reliable PLR.  
Conceptualization, K.K. and A.R.; methodology, K.K., S.L., and A.R.; writing-original draft preparation, K.K.; writing-review and editing, K.K., A.R., S.L., D.M., M.J.; validation, S.L., D.M., A.R., M.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the Indonesia Endowment Fund for Education (LPDP). This article/publication is based upon work from COST Action CA16235 PEARL PV supported by COST (European Cooperation in Science and Technology), including its Short Term Scientific Mission (STSM) grant conducted at EURAC Research in Bolzano, Italy. We also appreciate the expert support from Advanced Research on Innovations in Sustainability and Energy (ARISE) of the University of Twente. The authors declare no conflict of interest.