Global Climate Data Processing and Mapping of Degradation Mechanisms and Degradation Rates of PV Modules

Ascencio‐Vásquez, Julián; Kaaya, Ismail; Brecl, Kristijan; Weiß, Karl-Anders; Topič, Marko

doi:10.3390/en12244749

Cited by 56 publications

(58 citation statements)

References 26 publications

(38 reference statements)

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“…Although out of the scope of the present work, in practice there are other important effects on the energy production and return on investment of PV installations, such as the higher degradation rate with increased module temperatures ( Ascencio-Vásquez et al, 2019 ; Vazquez et al, 2017 ) and the geographical and seasonal variations of soiling rates ( García et al, 2011 ; Sarver et al., 2013 ; Ilse et al., 2019 ), as well as other loss mechanisms at the module and system levels. When PV module temperatures are above a certain threshold, it becomes economically advantageous to turn trackers away from the sun, reducing the risk of damage and also the energy production.…”

Section: Discussionmentioning

confidence: 99%

Location-Specific Spectral and Thermal Effects in Tracking and Fixed Tilt Photovoltaic Systems

et al. 2020

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The efficiency of photovoltaic modules in the field is generally lower than the efficiency under standard testing conditions due to temperature and spectral effects. Using the latest spectral dataset available from the National Solar Radiation Database, we report spectral correction factors ranging from-2% to 1.3% of the produced energy for silicon modules depending on location and collector geometry. We find that spectral effects favor trackers if silicon modules are used, but favor a fixed tilt instead if perovskites or CdTe are used. In high-irradiance locations, the energy yield advantage of silicon-based trackers is underestimated by 0.4% if spectral sensitivity effects are neglected. As the photovoltaic market grows to a multi-terawatt size, these seemingly small effects are expected to have an economic impact equivalent to tens of billions of dollars in the next few decades, far outweighting the cost of the required research effort.

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Section: Discussionmentioning

confidence: 99%

Location-Specific Spectral and Thermal Effects in Tracking and Fixed Tilt Photovoltaic Systems

et al. 2020

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“…The amount of rainfall may vary from place to place. It was found that below 20 mm of rainfall, the PV surface was not adequately cleaned off in a dry California climate (based on the Köppen-Geiger climate classification) [67][68][69]. Light drizzle type rainfall enhanced dust adhesion on the module, which converted this dust into mud, which was hard to remove without any mechanical cleaning methods [70].…”

Section: Effect Of Rain and Moisturementioning

confidence: 99%

Soiling Losses: A Barrier for India’s Energy Security Dependency from Photovoltaic Power

Ghosh

2020

Challenges

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Worldwide photovoltaic power generation is affected by deposited dust on photovoltaic (PV) systems, which creates soiling losses. In this work, factors that have a detrimental influence on dust deposition and an impact on PV systems performance were reviewed. The different ways that dust deposition can be a barrier for India’s energy security plan involving PV were also discussed. Different available cleaning techniques were also introduced. The nature, size, and morphology of dust particles vary with geographical location. Any increase of the PV tilt angle, or high wind speed and heavy rain showers reduce dust deposition. Deposited dust reduces the incident transmitted light on the PV, which has an adverse impact on the reduction of short circuit current. However, the open-circuit voltage has a reduced effect due to dust deposition. The enhancement of temperature caused by dust-covered PVs is still a debatable area. A universal cleaning technique is required to eliminate the soiling losses from PV. India has a solar mission to generate 100 GW of PV power by 2022. However, India’s poor air quality can undermine efforts to achieve this target.

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“…The spatially invariant inaccuracies of the underlying data set were therefore not addressed, as emphasis was instead put on the ML and DL models and their forecast accuracy, relative to the ERA5‐Land data. Furthermore, since the climate data used in the single‐WT forecasts concerns only a small geographical region, potential accuracy differences between distant data points [33] were avoided.…”

Section: Climate Datamentioning

confidence: 99%

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

Marinšek

Bajt

2020

IET Renewable Power Generation

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Wind is a highly unstable renewable energy source. Accurate forecasting can mitigate the effects of wind inconsistency on the electric grid and help avoid investments in costly energy storage infrastructure. Basing the predictions on open‐source forecast models and climate data also makes them entirely free of charge. The present work studies the feasibility of using two machine learning (ML) models and one deep learning (DL) model, random forest (RF) regression, support vector regression (SVR), and long short‐term memory (LSTM) for short‐term wind power forecasting based on the publicly accessible ERA5‐Land dataset. For each forecast model, a selection of hyperparameters is first tuned, followed by determining the best performing input data structure using surrounding data grid points and increasing the time interval of data affecting a single prediction. Both the ML models and the DL model perform better than the baseline (BL) model when forecasting wind speed up to 24 hours ahead. However, a reduced forecast duration is needed to achieve satisfactory wind turbine (WT) power output forecast accuracy. Most notably, the RF is able to produce 3‐hour forecasts with the combined WT power output prediction error amounting to less than 10 % of the WT's nominal power.

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Global Climate Data Processing and Mapping of Degradation Mechanisms and Degradation Rates of PV Modules

Cited by 56 publications

References 26 publications

Location-Specific Spectral and Thermal Effects in Tracking and Fixed Tilt Photovoltaic Systems

Location-Specific Spectral and Thermal Effects in Tracking and Fixed Tilt Photovoltaic Systems

Soiling Losses: A Barrier for India’s Energy Security Dependency from Photovoltaic Power

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

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