Photovoltaic (PV) systems are the cheapest source of electricity in sunny locations and nearly all European countries. However, the fast deployment of PV systems around the world is bringing uncertainty to the PV community in terms of the reliability and long-term performance of PV modules under different climatic stresses, such as irradiation, temperature changes, and humidity. Methodologies and models to estimate the annual degradation rates of PV modules have been studied in the past, yet, an evaluation of the issue at global scale has not been addressed so far. Hereby, we process the ERA5 climate re-analysis dataset to extract and model the climatic stresses necessary for the calculation of degradation rates. These stresses are then applied to evaluate three degradation mechanisms (hydrolysis-degradation, thermomechanical-degradation, and photo- degradation) and the total degradation rate of PV modules due to the combination of temperature, humidity, and ultraviolet irradiation. Further on, spatial distribution of the degradation rates worldwide is computed and discussed proving direct correlation with the Köppen-Geiger-Photovoltaic climate zones, showing that the typical value considered for the degradation rate on PV design and manufacturer warranties (i.e., 0.5%/a) can vary ± 0.3%/a in the temperate zones of Europe and rise up to 1.5%/a globally. The mapping of degradation mechanisms and total degradation rates is provided for a monocrystalline silicon PV module. Additionally, we analyze the temporal evolution of degradation rates, where a global degradation rate is introduced and its dependence on global ambient temperature demonstrated. Finally, the categorization of degradation rates is made for Europe and worldwide to facilitate the understanding of the climatic stresses.

A linear performance drop is generally assumed during the photovoltaic (PV) lifetime. However, operational data demonstrate that the PV module degradation rate ( Rd ) is often nonlinear, which, if neglected, may increase the financial uncertainty. Although nonlinear behavior has been the subject of numerous publications, it was only recently that statistical models able to detect change-points and extract multiple Rd values from PV performance time-series were introduced. A comparative analysis of six open-source libraries, which can detect change-points and calculate nonlinear Rd , is presented in this article. Since the real Rd and change-point locations are unknown in field data, 960 synthetic datasets from six locations and two PV module technologies have been generated using different aggregation and normalization decisions and nonlinear degradation rate patterns. The results demonstrated that coarser temporal aggregation (i.e., monthly vs. weekly), temperature correction, and both PV module technologies and climates with lower seasonality can benefit the change-point detection and Rd extraction. This also raises a concern that statistical models typically deployed for Rd analysis may be highly climatic- and technology-dependent. The comparative analysis of the six approaches demonstrated median mean absolute errors (MAE) ranging from 0.06 to 0.26%/year, given a maximum absolute Rd of 2.9%/year. The median MAE in change-point position detection varied from 3.5 months to 6 years.

In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with accurate input data. However, most of the PV systems do not include on-site high-class measurement devices for irradiance and other weather conditions. For this reason, the use of reanalysis-based or satellite-based data is currently of significant interest in the PV community and combining the data with decomposition and transposition irradiance models, the actual Plane-of-Array operating conditions can be determined. In this paper, we are proposing an efficient and accurate approach for PV output energy modelling by combining a new data filtering procedure and fast machine learning algorithm Light Gradient Boosting Machine (LightGBM). The applicability of the procedure is presented on three levels of irradiance data accuracy (low, medium, and high) depending on the source or modelling used. A new filtering algorithm is proposed to exclude erroneous data due to system failures or unreal weather conditions (i.e., shading, partial snow coverage, reflections, soiling deposition, etc.). The cleaned data is then used to train three empirical models and three machine learning approaches, where we emphasize the advantages of the LightGBM. The experiments are carried out on a 17 kW roof-top PV system installed in Ljubljana, Slovenia, in a temperate climate zone.