Power performance of high density photovoltaic module using energy balance model under high humidity environment

Choi, Jinho; Hyun, Jin-Hee; Lee, Wonbin; Bhang, Byung-Gwan; Min, Yong Ki; Ahn, Hyungkeun

doi:10.1016/j.solener.2020.10.022

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…However, wind speed and humidity are not taken into consideration since these are not directly related to the current and voltage equation of PV power generation. As shown in Figure 2, the humidity and wind speed have a relation to the module temperature, resulting in affecting the PV power generation indirectly [22,23]. They are less significant than solar radiation, module temperature, and ambient temperature.…”

Section: The Correlation Analysis Of Piot Data In the Photovoltaic Systemmentioning

confidence: 99%

Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Ahn

Park

2021

Energies

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Photovoltaic (PV) power fluctuations caused by weather changes can lead to short-term mismatches in power demand and supply. Therefore, to operate the power grid efficiently and reliably, short-term PV power forecasts are required against these fluctuations. In this paper, we propose a deep RNN-based PV power short-term forecast. To reflect the impact of weather changes, the proposed model utilizes the on-site weather IoT dataset and power data, collected in real-time. We investigated various parameters of the proposed deep RNN-based forecast model and the combination of weather parameters to find an accurate prediction model. Experimental results showed that accuracies of 5 and 15 min ahead PV power generation forecast, using 3 RNN layers with 12 time-step, were 98.0% and 96.6% based on the normalized RMSE, respectively. Their R2-scores were 0.988 and 0.949. In experiments for 1 and 3 h ahead of PV power generation forecasts, their accuracies were 94.8% and 92.9%, respectively. Also, their R2-scores were 0.963 and 0.927. These experimental results showed that the proposed deep RNN-based short-term forecast algorithm achieved higher prediction accuracy.

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Section: The Correlation Analysis Of Piot Data In the Photovoltaic Systemmentioning

confidence: 99%

Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Ahn

Park

2021

Energies

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“…28 Second, humidity has the potential to affect the atmosphere around the PV module and the module temperature, thereby affecting the power performance of the PV module. 29,30 Based on the proposed model, focus is on computed cell temperatures and Uvalues and a comparison between the two technologies system designs shown in Figure 7.…”

Section: Comparison To Field Measurementsmentioning

confidence: 99%

Heat loss coefficients computed for floating PV modules

Lindholm

Kjeldstad

Selj

et al. 2021

Progress in Photovoltaics

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For floating PV (FPV), the operating temperature of the PV modules has been a major source of uncertainty. As the operating temperature of PV modules affects their efficiency, knowledge of this parameter is critical in order to perform accurate energy yield assessment (EYA). This uncertainty is reflected in the scientific literature but has also hampered the bankability and realization of commercial FPV projects. Our work proposes a model that computes both the efficiency of heat loss to the environment for a given FPV technology and the operational cell temperature, needed to compute the module efficiency. The suggested model is applicable to different FPV technologies. We show that PV modules that are not in direct thermal contact with water have similar heat loss behavior as land-based PV. We thereafter investigate a specific technology in which the modules are mounted on a membrane resting directly on the water body. The model is validated against actual production and weather data from deployed FPV systems. Our results show that the water temperature impacts both FPV technologies, however to a smaller degree for the air-cooled system, where the wind is of greater influence. When in direct thermal contact with water, the water body provides superior cooling, and the resulting U-value of about 86.5 W/m 2 K is significantly larger than typical values reported for land-based modules.

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“…3, the power output is inversely proportional to the humidity. According to the article [47], the tested results verified that the humidity influences the PV solar modules efficiency; since humidity increases, the heat exchange rate decreased. Losses The solar energy generation system has different components; as mentioned in the introduction section, they all directly correlate with the PV solar module's overall performance.…”

Section: Effect Of Humiditymentioning

confidence: 86%

Recent Improvements of the PV Solar Energy Generation Performance

Takiso¹,

Manbecho²

2021

IJRTE

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PV solar energy is the upcoming king of the energy source in the world, which is the fastest growing, most available, sustainable, clean, and environmentally friendly renewable energy. The essential characteristic of PV solar energy is generating the maximum power at mid-day. At the same time, the energy demand is high during the daytime. Due to this, PV solar energy replaces the conventional energy demand at peak periods. The sun is the source of PV solar energy, and it changed into electricity directly by using solar cells, which are made from semiconductor materials called silicon. Therefore, PV solar energy plays a crucial role in providing usable energy, and as well as reducing carbon dioxide emissions. However, the solar energy generation systems not achieved the desired efficiency yet, because of many unsolved problems like weather conditions, losses, materials made by and so on. The aims of this paper is to review the current literature on the improvement of the PV solar energy generation system's overall performance. First, to figure out the existing challenges, like environmental factors and natural phenomena that affect the PV solar modules efficiency. Then it presents the techniques that are used to enhance the PV solar modules overall performance. Finally, to propose the best ways and techniques to improve the PV modules efficiency and suggest to further studies.

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Power performance of high density photovoltaic module using energy balance model under high humidity environment

Cited by 14 publications

References 16 publications

Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Deep RNN-Based Photovoltaic Power Short-Term Forecast Using Power IoT Sensors

Heat loss coefficients computed for floating PV modules

Recent Improvements of the PV Solar Energy Generation Performance

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