Comparison of Statistical and Deterministic Smoothing Methods to Reduce the Uncertainty of Performance Loss Rate Estimates

Ingenhoven, P.; Belluardo, Giorgio; Moser, David

doi:10.1109/jphotov.2017.2762523

Cited by 22 publications

(22 citation statements)

References 31 publications

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“…The absolute PLR (Equation ) is independent of the initial starting value of the chosen metric. The absolute PLR gives an indication of the absolute loss rate, but it is important that the fitting parameter β 0 is also given 12 . The relative PLR (Equation ) makes it easier to generalize the findings to the energy yield of the array using the initial yield of the plant.…”

Section: Methodsmentioning

confidence: 99%

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Lindig

Moser

Curran

et al. 2021

Progress in Photovoltaics

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The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

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Section: Methodsmentioning

confidence: 99%

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Lindig

Moser

Curran

et al. 2021

Progress in Photovoltaics

Self Cite

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“…Some examples were considered in Section I, including linear regression, seasonal decomposition, or the year-on-year approach. The options available to PV analysts are growing all the time, particularly with recent advances in local regression, binning methods, and machine-learning algorithms [2], [13], [50], [51].…”

Section: Decision Treesmentioning

confidence: 99%

“…Degradation rates (R d ) quantify the slow and gradual loss of performance and are typically expressed relative to previous performance in %/year. These rates are also known by less ambiguous terms such as performance loss rates or rates of change [2], [3], and we take the convention here of a negative value indicating performance loss.…”

Section: Introductionmentioning

confidence: 99%

Reducing Interanalyst Variability in Photovoltaic Degradation Rate Assessments

Jordan

Luo

Jain

et al. 2020

IEEE J. Photovoltaics

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The economic return on investment of a commercial photovoltaic system depends greatly on its performance over the long term and, hence, its degradation rate. Many methods have been proposed for assessing system degradation rates from outdoor performance data. However, comparing reported values from one analyst and research group to another requires a common baseline of performance; consistency between methods and analysts can be a challenge. An interlaboratory study was conducted involving different volunteer analysts reporting on the same photovoltaic performance data using different methodologies. Initial variability of the reported degradation rates was so high that analysts could not come to a consensus whether a system degraded or not. More consistent values are received when written guidance is provided to each analyst. Further improvements in analyst variance was accomplished by using the free open-source software RdTools, allowing a reduction in variance between analysts by more than

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“…Em PTC considera-se a temperatura ambiente como 20 °C, a irradiância como 1000 W/m², e a velocidade do vento como 1 m/s (Kim et al, 2019). Segundo Ingenhoven et al (2018), os cálculos de taxa de desempenho por meio da PVUSA possuem como vantagem em relação a outras metodologias a consideração da velocidade do vento, o que faz com que ela funcione melhor para tecnologias com presença de altos coeficientes de temperatura.…”

Section: Introductionunclassified

Análise Comparativa de Diferentes Metodologias de Cálculo de Taxa de Desempenho em Sistemas Fotovoltaicos Instalados em Ponta Grossa-PR

Cremasco

Takeuchi

Leludak

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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Duas formas de análise da taxa de desempenho de sistemas fotovoltaicos são o cálculo convencional e a utilização de "Photovoltaic for Utility Systems Applications" (PVUSA). Portanto, este estudo aborda uma análise de quatro painéis fotovoltaicos de quatro tecnologias distintas instalados em Ponta Grossa- PR por meio destas duas metodologias. Para a realização das análises, foram coletados dados de geração, de irradiação no plano inclinado, velocidade do vento, e temperatura ambiente. Primeiramente, os dados de geração mensal e irradiação média mensal foram utilizados para o cálculo da taxa de desempenho convencional. Em um segundo momento, para a utilização da metodologia PVUSA nas condições "PVUSA Test Conditions"(PTC), os dados horários foram separados em valores de irradiação no plano inclinado superiores a 700 Wh/m² (PVUSA 700) e superiores a 800 Wh/m² (PVUSA 800). Como resultado, os valores obtidos por PVUSA 800 obtiveram menores diferenças percentuais em relação a metodologia de cálculo convencional do que os com PVUSA 700. Além disto, no caso da utilização da metodologia convencional e da PVUSA 700 a tecnologia com melhor taxa de desempenho foi telureto de cádmio (CdTe), e a tecnologia com menor taxa de desempenho em dois meses foi policristalina (p-Si).

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Comparison of Statistical and Deterministic Smoothing Methods to Reduce the Uncertainty of Performance Loss Rate Estimates

Cited by 22 publications

References 31 publications

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Reducing Interanalyst Variability in Photovoltaic Degradation Rate Assessments

Análise Comparativa de Diferentes Metodologias de Cálculo de Taxa de Desempenho em Sistemas Fotovoltaicos Instalados em Ponta Grossa-PR

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