Impacts of Cloud Cover and Dust on the Performance of Photovoltaic Module in Niamey

Bonkaney, Abdoulatif; Madougou, Saïdou; Adamou, Rabani

doi:10.1155/2017/9107502

Cited by 51 publications

(39 citation statements)

References 8 publications

(7 reference statements)

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“…In the same way at the moment of rainfall (Figure 10 There is also an increase in Diffuse when the sky is overcast. These results confirm those obtained by Abdoulatif Bonkaney et al (2017) [9].…”

Section: Monthly Variations Of Average Values Of Total Sunning On a Hsupporting

confidence: 93%

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Evaluation of Solar Potential at Niamey: Study Data of Insolation from 2015 and 2016

Dankassoua¹,

Madougou²,

Yahaya³

2017

SGRE

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Solar energy is a very abundant renewable energy source during the day. The solar energy received in a given point of the Earth is function of the time, the season and the latitude of the point. It has been proven that the solar energy received in one day by our planet is thirty times higher than the annual global energy consumption. Africa is one of the sunniest continents of the world. Nowadays, solar energy is attracting particular attention in the implementation of the energy policies. This renewable source is a key solution to world energy problems, especially in the context of global warming. Niger is identified as among the sunniest zones of the World. Knowledge of solar potential is one of the crucial parameters to master for energy applications. In this study, continuous measurements (at intervals of 5 minutes over 24 hours) of solar radiation have been carried out on the site of the National Center of Solar Energy of Niamey. These measurements were taken using the pyranometers, allowed us to collect the values of the daily global sunshine on a horizontal plane and on an inclined plane of the years 2015 and 2016. The treatment and the exploitation of these data allowed us to determine the daily and monthly duration of sunshine, then the impact of the clouds and dust on the solar radiation, to evaluate the solar potential of the site and determine the variations of this solar potential as a function of time. The results showed that the solar potential was very favorable for many solar applications.

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Section: Monthly Variations Of Average Values Of Total Sunning On a Hsupporting

confidence: 93%

“…In this paper, authors studied the impacts of cloud cover and dust on the performance of photovoltaic module in Niamey. Results show that dust accumulation has a great effect on decreasing the daily energy yield of the unclean module [9]. But this effect is a long-term effect.…”

Section: Literature Reviewmentioning

confidence: 94%

Evaluation of Solar Potential at Niamey: Study Data of Insolation from 2015 and 2016

Dankassoua¹,

Madougou²,

Yahaya³

2017

SGRE

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“…Specific to the United States, in addition to snow coverage in the Winter, future research should consider the potential for pollen build up in the Spring, tree shading during the Summer, and the soiling of leaves and debris in the Fall (Thevenard and Pelland 2013). For other dry-climate countries, future research should investigation better methods for forecasting dust and cloud coverage (Bonkaney et al 2017). Furthermore, future research should focus on how to make the models more user friendly, specific to derate estimation, to enhance the benefit to additional stakeholders such as home appraisers, realtors, insurance underwriters, solar contractors, and utility companies.…”

Section: Discussionmentioning

confidence: 99%

Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error

Bosman

Darling

2018

Environ Sci Pollut Res

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The advent of modern solar energy technologies can improve the costs of energy consumption on a global, national, and regional level, ultimately spanning stakeholders from governmental entities to utility companies, corporations, and residential homeowners. For those stakeholders experiencing the four seasons, accurately accounting for snow-related energy losses is important for effectively predicting photovoltaic performance energy generation and valuation. This paper provides an examination of a new, simplified approach to decrease snow-related forecasting error, in comparison to current solar energy performance models. A new method is proposed to allow model designers, and ultimately users, the opportunity to better understand the return on investment for solar energy systems located in snowy environments. The new method is validated using two different sets of solar energy systems located near Green Bay, WI, USA: a 3.0-kW micro inverter system and a 13.2-kW central inverter system. Both systems were unobstructed, facing south, and set at a tilt of 26.56°. Data were collected beginning in May 2014 (micro inverter system) and October 2014 (central inverter system), through January 2018. In comparison to reference industry standard solar energy prediction applications (PVWatts and PVsyst), the new method results in lower mean absolute percent errors per kilowatt hour of 0.039 and 0.055%, respectively, for the micro inverter system and central inverter system. The statistical analysis provides support for incorporating this new method into freely available, online, up-to-date prediction applications, such as PVWatts and PVsyst.

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“…Low values are obtained for December-February and August. The low values in December-February are a result of the winter season while in August, it is due to high cloudy conditions that blocks the incoming solar radiation [30]. Table 1 summarizes the relevant statistics for these weather variables.…”

Section: Journal Of Energymentioning

confidence: 99%

Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger

Bonkaney¹

2020

Journal of Energy

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This study examines the impacts of relevant factors (climatic and nonclimatic) on the monthly electricity consumption (MEC) in four major cities in Niger using simple multiple linear regressions (MLRs). Parameters such GDP/capita, air temperature (Tmean), relative humidity (RH), wind speed (WSP), solar radiation (SR), precipitation, and clearness index (K) are used. In addition, two heat indices, heat index (HI) and discomfort index (DI) are calculated to take into account the impacts of high humidity in conjunction with high ambient temperature. Hence, three different models were derived from the aforementioned variables. The three models have been tested using the k-folds cross-validation. Results show that the model with primitive variables such GDP per capita, Tmean, RH, SR, and WSP perform better than the other two models with a coefficient determination R 2 equal to 0.87, 0.854, 0.833, and 0.551 for Niamey, Maradi, Zinder, and Agadez, respectively. According to the month considered, the mean absolute percentage error can give a small error for specific combinations of climate variables. The variables such as precipitation and clearness index are found to be not statistically significant.

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Impacts of Cloud Cover and Dust on the Performance of Photovoltaic Module in Niamey

Cited by 51 publications

References 8 publications

Evaluation of Solar Potential at Niamey: Study Data of Insolation from 2015 and 2016

Evaluation of Solar Potential at Niamey: Study Data of Insolation from 2015 and 2016

Performance modeling and valuation of snow-covered PV systems: examination of a simplified approach to decrease forecasting error

Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger

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