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We present the results from multi-wavelength observations of a transient discovered during the follow-up of S191213g, a gravitational wave (GW) event reported by the LIGO-Virgo Collaboration as a possible binary neutron star merger in a low latency search. This search yielded SN2019wxt, a young transient in a galaxy whose sky position (in the 80\% GW contour) and distance ($\sim$150\,Mpc) were plausibly compatible with the localisation uncertainty of the GW event. Initially, the transient's tightly constrained age, its relatively faint peak magnitude ($M_i \sim -16.7$\,mag) and the $r-$band decline rate of $\sim 1$\,mag per 5\,days appeared suggestive of a compact binary merger. However, SN2019wxt spectroscopically resembled a type Ib supernova, and analysis of the optical-near-infrared evolution rapidly led to the conclusion that while it could not be associated with S191213g, it nevertheless represented an extreme outcome of stellar evolution. By modelling the light curve, we estimated an ejecta mass of $\sim 0.1\,M_\odot$, with $^{56}$Ni comprising $\sim 20\%$ of this. We were broadly able to reproduce its spectral evolution with a composition dominated by helium and oxygen, with trace amounts of calcium. We considered various progenitors that could give rise to the observed properties of SN2019wxt, and concluded that an ultra-stripped origin in a binary system is the most likely explanation. Disentangling electromagnetic counterparts to GW events from transients such as SN2019wxt is challenging: in a bid to characterise the level of contamination, we estimated the rate of events with properties comparable to those of SN2019wxt and found that $\sim 1$ such event per week can occur within the typical GW localisation area of O4 alerts out to a luminosity distance of 500\,Mpc, beyond which it would become fainter than the typical depth of current electromagnetic follow-up campaigns., By the ENGRAVE collaboration (engrave-eso.org). 35 pages, 20 figures, final version accepted by A&A

Contains fulltext : 284232.pdf (Publisher’s version ) (Closed access) The COVID-19 pandemic (and its aftermath) highlights a critical need to communicate health information effectively to the global public. Given that subtle differences in information framing can have meaningful effects on behavior, behavioral science research highlights a pressing question: Is it more effective to frame COVID-19 health messages in terms of potential losses (e.g., "If you do not practice these steps, you can endanger yourself and others") or potential gains (e.g., "If you practice these steps, you can protect yourself and others")? Collecting data in 48 languages from 15,929 participants in 84 countries, we experimentally tested the effects of message framing on COVID-19-related judgments, intentions, and feelings. Loss- (vs. gain-) framed messages increased self-reported anxiety among participants cross-nationally with little-to-no impact on policy attitudes, behavioral intentions, or information seeking relevant to pandemic risks. These results were consistent across 84 countries, three variations of the message framing wording, and 560 data processing and analytic choices. Thus, results provide an empirical answer to a global communication question and highlight the emotional toll of loss-framed messages. Critically, this work demonstrates the importance of considering unintended affective consequences when evaluating nudge-style interventions. 26 p.

8th World Conference on Photovoltaic Energy Conversion; 1413-1416, Degradation is one of the critical topics in PV performance assessments. It affects the lifetime yield and bankability and can always signal underperformance events or trigger a PV module warranty claim. To support this issue, holistic Technical Asset Management tools are needed for most PV stakeholders to keep track of the optimal performance of PV systems. One example is the EnOS™ Advanced Analytics for Solar, which enabled operators, asset managers, and others to get highly accurate and detailed energy loss calculations based on loss breakdown tools. This tool also includes sections addressing the PV module degradation estimations and DC health in the context of degradation and module health. This article focuses on steps beyond typical degradation assessments, including a robust approach to computing degradation rates of operating PV systems using diverse combinations of statistical methods, filtering strategies and irradiance data sources, followed by the introduction of the first steps toward a data-driven PV module warranty claim approach. A case study of an entire operating system is included proving the robustness and highlighting the need for those further steps for PV module degradation assessment.

8th World Conference on Photovoltaic Energy Conversion; 609-613, Ambient water can be absorbed by photovoltaic (PV) modules, affecting their degradation. This paper presents a two-step approach to evaluate the moisture profile inside fielded PV modules around the world. The first step consists of moisture ingress simulations at more than 17000 global locations using a cluster, running up to 160 simulations in parallel. Results of the simulations show a low R2-value of 0.65 when correlating the ambient relative humidity and the saturation moisture concentration in front of the cell, emphasizing the need for such simulations to provide more accurate inputs for degradation modelling. In the second step, we train a machine learning algorithm to obtain a model finding a better correlation between the important climatic parameters and the simulation results. The ML algorithm can accurately find such a correlation, shown by the R2-value of 0.98. We use the algorithm to further predict water ingress and interpolate the results to other global locations in a higher spatial resolution of the climatic input parameters.

38th European Photovoltaic Solar Energy Conference and Exhibition; 1028-1032, Identifying the main factors for PV module performance losses, degradation and failures are still under research. Researchers have developed physical models to characterize the weather and the local environment where PV modules operate. , Indeed, published physical models can help to estimate PV long-term yield and degradation rates. These models highly depend on the operating PV module temperature, UV irradiance, and relative humidity as main inputs. In most cases, these input variables need to be modelled. For example, several models to estimate PV module temperature are available. However, improvements in their accuracy are still needed. Therefore, in this paper, we apply Machine Learning approaches to identify the combination of climate variables needed to model the PV module temperature. We then propose a modifiedtFaiman model that includes the effect of UV irradiance. Lastly, the modified model is benchmarked with other physical temperature models and shows an improvement over other models.

38th European Photovoltaic Solar Energy Conference and Exhibition; 979-988, In the framework of the new H2020 project TRUST PV, the Cost Priority Number methodology, a cost-based failure modes & effect analysis method, is tailored for its application onto real-case studies during the operational phase of a PV plant. Thereby, it is possible to calculate the energetic and economic losses for each individual failure by combining actual technical characteristics of the failure tickets, the operational costs of fixing those failures, and the power loss due to the failure occurrence. In this work, different empirical, machine learning and univariate models have been tested to find the optimal model to impute power data for all possible scenarios of missing performance data. The parameter space comprises of the amount and type of missing data, the ratio between training and test set and the predictor availability. Overall, the most desirable setting for highly accurate data imputation is the availability of a neighbouring string/plant or measured climate data as predictor with a high training/test set ratio for small test sets and ratios between 80/20 to 50/50 for bigger test sets (longer than 12 hours). In general, machine learning imputation models and empirical power models perform similar for smaller test set sizes and machine learning models are preferrable for bigger data gaps. Univariate methods should be avoided if possible.

38th European Photovoltaic Solar Energy Conference and Exhibition; 1088-1091, Total solar eclipses are events that rarely occur but can highly impact the operation of electrical grids. During a few minutes, the Sun can be fully covered by the moon, blocking the sun rays towards the Earth surface. This phenomenon has a considerable effect on the electrical generation of PV systems and, depending on the level of deployment of this technology, can compromise the stability of electrical grids, causing perturbations and making the grid prone to failures. For this reason, the accurate yield forecasting of PV plants during a solar eclipse can help grid planners to handle this event in the most cost-efficient way. In this work, we present preliminary results of the modelling and quantification of PV plants energy production and losses during total solar eclipses. The TDP model is used to model the PV performance under clear-sky conditions. The results show around 10% of daily PV energy yield and 13% of daily irradiation during the total solar eclipse seen in Chile on the 14th December 2020.

Prediction of the service lifetime of Photovoltaic (PV) modules is crucial for all PV stakeholders. PV manufacturers benefit from setting realistic warranties and researchers can improve the durability of PV materials and components in order to achieve a desired lifetime. To develop reliable predictive models, a thorough understanding of the different sources of uncertainties in degradation rates determination and lifetime prediction is indispensable. In this paper, we assess different sources of uncertainties in PV degradation rates and lifetime predictions using physical models. Three major sources of uncertainties are discussed: due to climate variables estimation, due to PV modules reliability and due to degradation rate models. Degradation rates evaluated using modelled and measured climatic variables are benchmarked to assess the uncertainties due to climatic input variables. It is found that module temperature estimation leads to the highest uncertainties in degradation rates of 1.7% to 14.5% (depending on the temperature model and also on the location) followed by relative humidity 2.4% to 12.2%. UV modelling showed the least effect on degradation rates variations of only 0.1% to 5%. The variations due to the different PV module reliability and degradation rate models are evaluated using measured PV performance data. It is shown that these variations can be as high as 65.5% and 59.4% respectively, even if the models are calibrated using the same data.

38th European Photovoltaic Solar Energy Conference and Exhibition; 667-669, In the framework of the new H2020 project TRUST PV, a benchmarking methodology is being developed. The objective is analyzing how different contextual parameters – such as micro-climate and environment, different assets composed of different components from different manufactures in different configurations – influence the distribution of performance Key Performance Indicators (KPIs). This is achieved by applying big data analytics to over 6.5 GW of solar monitoring data coming from more than 10.000 PV sites monitored through 3E’s asset management platform SynaptiQ. In this paper, the data requirements, and data architecture for this type of benchmarking are presented as well as several preliminary case studies. However, the work is still ongoing within the TRUST PV project and needs to be further extended and validated. The end goal of this component-benchmarking tool is to support technical decisions of different stakeholders (manufacturers, designers, operators, financial entities, etc.), to improve the assumptions taken in PV yield and degradation modelling.

37th European Photovoltaic Solar Energy Conference and Exhibition; 1401-1404, Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the PV module degradation rate. Since the real value of degradation rate is unknown, a variety of unvalidated values has been reported in the literature. As such, the PV industry commonly treats this metric in an assumptive manner based on a statistically extracted range from the literature. However, the accuracy and uncertainty of degradation rate depends on a number of parameters including seasonality in respect to the local climatic conditions and also the response of a particular PV technology. In addition, the selection of data pipeline and statistical method may compound on the accuracy and uncertainty. In order to provide insights, a framework of bulk simulations of PV performance datasets using data from different climates is under development. Known degradation rates are emulated and large parametric studies are conducted in order to observe the convergence time on different PV module types based on several selection criteria such as performance metric, statistical method, etc. The preliminary results that are presented in this paper confirm that, indeed, climates and PV module types with typically lower seasonality can provide accurate degradation rate results in a shorter time period, compared to locations and PV module types that exhibit higher seasonality. As expected, the selection of data pipeline (e.g. metric, temperature correction, etc.) and statistical method also has a strong influence and therefore, introduces additional challenges.

37th European Photovoltaic Solar Energy Conference and Exhibition; 1567-1572, In this work, we present a comparison of Performance Loss Rate calculation methodologies based on originally 8,367 PV system datasets, primarily installed in central Europe. The system data are summarized in the Pearl PV database. Pearl PV is a European COST action aiming at improving the energy performance and reliability of PV systems. The datasets consist of only power measurements. Significant dataset filtering was necessary to remove short time series as well as outliers and resulted in the exclusion of over 90% of the datasets. Three different PLR calculation methodologies have been tested, namely statistical clear-sky fitting, the Year-on-Year approach and seasonal and trend decomposition. Depending on the chosen approach, average system performance losses between -0.75%/a and -1.1%/a have been recorded.