Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system

Kumar, P. Marish; Saravanakumar, R.; Karthick, Alagar; Mohanavel, V.

doi:10.1007/s11356-021-16398-6

Cited by 56 publications

(27 citation statements)

References 45 publications

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“…Instead of having many hyperparameters, the VGG16 model supports 16 layers and focuses on the convolution layers of 3 × 3 filters in stride one and padding along with Max-pooling layers of 2x2 filters in stride 2. So the F 1 score of VGG 16 performs better for both COVID and non-COVID classes compared to other CNN models[ 29 - 32 ].…”

Section: Performance Evaluation Measuresmentioning

confidence: 99%

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Kogilavani¹,

Jayagopal

Sandhiya³

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

113

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SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body’s respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient’s computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with “COVID” and “Non-COVID.” The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

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Section: Performance Evaluation Measuresmentioning

confidence: 99%

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Kogilavani¹,

Jayagopal

Sandhiya³

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

113

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“…Region-based processing is faster. R-CNN [ 13 ] employs the region proposal network (RPN) [ 14 ], a tiny CNN. It predicts whether there is a sliding on the last feature map object or not and also predicts the boundary of those objects.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Haseena¹,

Subbaraj²,

Madavan³

et al. 2022

Computational and Mathematical Methods in Medicine

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In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D -dimensional characteristics from the source face image, followed by the feature selection strategy. The face’s distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person’s age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

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“…Besides the previous methods, Recurrent Neural Network (RNN), 33 Feed-Forward Neural Network (FFNN), 34 and Feed-Back Neural Network (FBNN) have been deployed to predict the PV generation at various time horizons. 35 For example, Kumar et al 36 developed three real-time prediction models, namely the Elman Neural Network, FFNN, and Generalized Regression Neural Network (GRNN), for the short-term power production prediction of a Semi-Transparent PV (STPV) system. The three developed models used the ambient temperature, solar irradiance, and wind speed as the input parameters to forecast the output power for an STPV system in India.…”

Section: Literature Reviewmentioning

confidence: 99%

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

Al‐Dahidi

Muhsen

Sari

et al. 2022

Advances in Mechanical Engineering

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The world is becoming more reliant on renewable energy sources to satisfy its growing energy demand. The primary disadvantage of such sources is their significant uncertainty in power production. As appropriate energy production planning and scheduling necessitate a solid and confident assessment of renewable power production, the necessity for developing reliable prediction models grows by the day. This paper proposes an adaptive approach-based ensemble for 1-day ahead production prediction of solar Photovoltaic (PV) systems. Different ensembles of Artificial Neural Networks (ANNs) prediction models are established, whose architectures (number of the ANNs that comprise the ensembles) and configurations (number of hidden nodes required by the ANNs models of the ensembles) change adaptively at each hour h, h∈ [1, 24] of a day, for accommodating the hour seasonality in the solar PV data and, thus, enhancing the 1 day-ahead predictions accuracy. The suggested approach is tested on a 264 kW solar PV system installed at Applied Science Private University, Jordan. Its prediction performance is evaluated, particularly for different weather conditions (seasons) experienced by the concerned PV system, using standard performance metrics. Results show the effectiveness of the suggested approach in predicting solar PV power production and its superiority compared to another prediction approach of the literature that uses single ANNs at each hour h of the day. Specifically, for 1-day ahead prediction, the obtained enhanced accuracy, on average, was around 8%–10% on the test “unseen” datasets.

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Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system

Cited by 56 publications

References 45 publications

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

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