Effect of cloudiness on the production of electricity by photovoltaic panels

Chrobák, Pavel; Skovajsa, Jan; Zálešák, Martin

doi:10.1051/matecconf/20167602010

Cited by 8 publications

(7 citation statements)

References 1 publication

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“…As Chrobak el al. point out [26], this information is essential for determining the magnitude with which the clouds impede the PV power production. Therefore, acquiring some additional cloud-related features might be useful for increasing the PV output forecasting accuracy.…”

Section: A Data Collection and Preprocessingmentioning

confidence: 99%

Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features

et al. 2023

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Section: A Data Collection and Preprocessingmentioning

confidence: 99%

Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features

et al. 2023

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“…hailstorms, which can damage the panes of glass in the panels [26,27], -the heavy or frequent cloudiness [28], -poor solar irradiance [29], -strong winds [30], -fire in the installation caused by lightning [22,25].…”

Section: Operation and Failures Of Photovoltaic Installationsmentioning

confidence: 99%

Most Searched Topics in the Scientific Literature on Failures in Photovoltaic Installations

Kut

Pietrucha-Urbanik

2022

Energies

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Photovoltaic installations (PVs) are currently one of the fastest-growing sources of renewable energy. Expanded forms of financial support and higher electricity prices have resulted in a large increase in its installed capacity. PV installations are increasingly being ordered by industry and privates, often for installations capacity of several hundred kilowatts. In addition to the advantages, photovoltaic installations also have drawbacks. One of these is that the increase in the voltage in the power grid leads to the exclusion of individual installations from the grid. An important issue in the operation of photovoltaic installations is also their reliability during their lifetime. The reliability of photovoltaic installations depends on the random nature of the cloud cover as well as the material’s mechanical degradations. This paper presents a literature analysis using Citespace software in terms of reliability. A detailed bibliometric analysis has been performed to outline the main drawbacks of the PV installations cited by researchers. This literature review forms the basis for further analysis. The paper also presents a new approach to implementing the Multiple-Criteria Decision Analysis (MCDA) method for assessing the risk of failure of PV panels. The obtained results showed the main interests of scientists in the field of failure analysis of photovoltaic installations and countries having the largest share in research on this issue. The applied Analytic Hierarchy Process (AHP) analysis enables supporting the process of managing photovoltaic installations by analyzing installation operations in terms of reliability as reliability impacts the profitability of investments and operating costs. The proposed method can be used by the operators of photovoltaic installations or farms.

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“…Наприклад, у хмарні дні не доцільно використовувати потужні електроприлади (пральну машину, електронагрівачі, зарядку електромобіля тощо) без крайньої необхідності, а всю цю активність можна запланувати у сонячні дні. Тому розробка системи прогнозування потужності сонячних батарей для управління енерговитратами розумного будинку є актуальною задачею, яка привертає увагу багатьох дослідників [14][15][16].…”

Section: вступunclassified

System of Forecasting the Smart Home Solar Battery Power

Olenych¹,

Pavlyk²,

Martsiniv³

2022

ELIT

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У роботі запропоновано систему прогнозування потужності сонячної батареї розумного будинку на основі аналізу погодних даних. Для аналізу використовуються отримані за допомогою он-лайн сервісу OpenWeatherMap значення ступеня хмарності у заданій місцевості та відповідні значення потужності фотоелектричного модуля Buheshui JZ-110X69 для різних погодних умов. Прогнозування здійснюється за допомогою кореляційного аналізу методом Пірсона. На основі експериментальних даних визначено коефіцієнт лінійної кореляції та параметри лінії регресії. Встановлено високий ступінь оберненої лінійної кореляції хмарності та потужності фотоелектричного модуля. Для прогнозу потужності сонячних батарей використано рівняння лінійної регресії. Отримані результати можуть бути використані для формування рекомендованих сценаріїв раціонального використання енергоресурсів.

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Effect of cloudiness on the production of electricity by photovoltaic panels

Cited by 8 publications

References 1 publication

Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features

Design and Development of a Short-Term Photovoltaic Power Output Forecasting Method Based on Random Forest, Deep Neural Network and LSTM Using Readily Available Weather Features

Most Searched Topics in the Scientific Literature on Failures in Photovoltaic Installations

System of Forecasting the Smart Home Solar Battery Power

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