Dust Effect on Photovoltaic Output Performance: Comparative Analysis and A Case Study in Dhaka, Bangladesh

Kabir, Ahsan; Islam, Roubaiath; Nazifa, Saraban; Choudhury, Rukhsana

doi:10.1109/acmi53878.2021.9528120

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…Moreover, dust causes a temperature variation, resulting in a slight difference in short-circuit current and a decrease of voltage in open circuit, both decreased by 15 to 20% and 2 to 6%, respectively [7]. In another study, the dust effect of different PV modules showed that 33% of output power was reduced with a dust density of 4.25 mg/cm 2 in a-Si and CdTe type modules [8]. It has been also observed that dirty Si solar cells had a reduced efficiency of 66% over a period of six months.…”

Section: Introductionmentioning

confidence: 97%

“…In Southeast Asian, the peak power from PV was reduced by 18% due to dust in a study in Malaysia [14], whereas the dust accumulation on the PV panel decreased power by 10.8% with a mean relative humidity of approximately 52.24% in an experiment in Indonesia [15]. According to studies in the Middle East, maximum output power decreased by 34% [8] in Kuwait, whereas an experiment in Saudi Arabia showed that the output of PV modules might decrease by 26% to 40% [16]. A study conducted in Iran showed that a lack of rain for 70 days causes 6.0986 (g/m 2 ) of dust to accumulate on the surface and reduce output power by 21.47%, which is a 289-kWh energy reduction in provided energy for each 78 4.845 kW PV system [17].…”

Section: Introductionmentioning

confidence: 98%

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SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

Onim¹,

Sakif

Ahnaf³

et al. 2022

Energies

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Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%. Hence, both the dataset and SolNet can be used as benchmarks for future research endeavors. Furthermore, the classes of the dataset can also be expanded for multiclass classification. At the same time, the SolNet model can be fine-tuned by tweaking the hyperparameters for further improvements.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 98%

SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

Onim¹,

Sakif

Ahnaf³

et al. 2022

Energies

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An interpretable Bayesian deep learning-based approach for sustainable clean energy

Ezzat,

Ahmed,

Soliman

et al. 2024

Neural Comput & Applic

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Sustainable Development Goal 7 is dedicated to ensuring access to clean and affordable energy that can be utilized in various applications. Solar panels (SP) are utilized to convert sunlight into electricity, acting as a renewable energy source. It is important to keep SP clean to obtain the required performance, as the accumulation of snow and dust on SP greatly affects the amount of electricity generated. On the other hand, excessive cleaning has some detrimental effects on the SP, therefore cleaning should only be done when necessary and not on a regular basis. Consequently, it is critical to determine whether the cleaning procedure is necessary by automatically detecting the presence of dust or snow on the panels while avoiding inaccurate predictions. Research efforts have been made to detect the presence of dust and snow on SP, but most of the proposed methods do not guarantee accurate detection results. This paper proposes an accurate, reliable, and interpretable approach called Solar-OBNet. The proposed Solar-OBNet can detect dusty SP and snow-covered SP very efficiently and be used in conjunction with the methods used to clean SP. The proposed Solar-OBNet is based on a Bayesian convolutional neural network, which enables it to express the amount of confidence in its predictions. Two measurements are used to estimate the uncertainty in the outcomes of the proposed Solar-OBNet, namely predictive entropy and standard deviation. The proposed Solar-OBNet can express confidence in the correct predictions by showing low values for predictive entropy and standard deviation. The proposed Solar-OBNet can also give an uncertainty warning in the case of erroneous predictions by showing high values of predictive entropy and standard deviation. The proposed Solar-OBNet’s efficacy was verified by interpreting its results using a method called Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). The proposed Solar-OBNet has achieved a balanced accuracy of 94.07% and an average specificity 95.83%, outperforming other comparable methods.

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Dust Effect on Photovoltaic Output Performance: Comparative Analysis and A Case Study in Dhaka, Bangladesh

Cited by 2 publications

References 32 publications

SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

An interpretable Bayesian deep learning-based approach for sustainable clean energy

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