The unpredictable nature of photovoltaic solar power generation, caused by changing weather conditions, creates challenges for grid operators as they work to balance supply and demand. As solar power continues to beco...
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The unpredictable nature of photovoltaic solar power generation, caused by changing weather conditions, creates challenges for grid operators as they work to balance supply and demand. As solar power continues to become a larger part of the energy mix, managing this intermittency will be increasingly important. This paper focuses on identifying daily photovoltaic power production patterns to gain new knowledge of the generation patterns throughout the year based on unsupervised learning algorithms. The proposed data-driven model aims to extract typical daily photovoltaic power generation patterns by transforming the high dimensional temporal features of the daily PV power output into a lower latent feature space, which is learned by a deep learning autoencoder. Subsequently, the Partitioning Around Medoids (PAM) clustering algorithm is employed to identify the six distinct dominant patterns. Finally, a new algorithm is proposed to reconstruct these patterns in their original subspace. The proposed model is applied to two distinct datasets for further analysis. The results indicate that four out of the identified patterns in both datasets exhibit high correlation (over 95%) and temporal trends. These patterns correspond to distinct weather conditions, such as entirely sunny, mostly sunny, cloudy, and negligible power generation days, which were observed approximately 61% of the analyzed period. These typical patterns can be expected to be observed in other locations as well. Identified PV power generation patterns can improve forecasting models, optimize energy management systems, and aid in implementing energy storage or demand response programs and scheduling efficiently.
The COVID-19 pandemic highlighted the urgent need for rapid and efficient screening methods, leading to a growing demand for alternatives to resource-intensive RT-PCR tests. Among these, intelligent, contact-free auto...
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The COVID-19 pandemic highlighted the urgent need for rapid and efficient screening methods, leading to a growing demand for alternatives to resource-intensive RT-PCR tests. Among these, intelligent, contact-free automated systems emerged as a promising solution for quick preliminary COVID-19 detection. This study introduces the COVID-19 Multi-Layer Ensemble framework (C19-MLE), designed to enhance the accuracy of COVID-19 detection. The approach begins with a 2D convolutional neural network (CNN) combined with a variation autoencoder for precise classification of cough sounds. Additionally, a UNet-based encoder-decoder architecture is used for segmenting chest X-ray images. These segmented images are then classified using two models, ResNet-50 and Inception V3, and their results are combined using an ensemble learning technique. This first-layer ensemble achieves an impressive accuracy of 98.5% in classifying chest X-rays. Meanwhile, the proposed 2D CNN model for cough classification achieves an accuracy of 97.79%. The second-layer ensemble, which fuses the results of both chest X-ray and cough classifications using a meta-classifier with a hard prediction and weighted sum-rule technique, achieves a remarkable overall accuracy of 99.89%. The C19-MLE framework demonstrates the powerful synergy between cough audio signals and chest X-ray images, providing a highly accurate method for preliminary and post-screening COVID-19 diagnosis. The high accuracy of this model highlights its potential as a crucial tool for early disease detection and prevention, especially in settings where resources are limited.
With the increasing penetration of photovoltaic generation (PV), its output has impacted the power grid significantly. However, due to complex weather factors, PV output is intermittent and uncertain, making it challe...
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This paper presents an end-to-end methodology that can be used in the disaster response process. The core element of the proposed method is a deeplearning process which enables a helicopter landing site analysis thro...
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This paper presents an end-to-end methodology that can be used in the disaster response process. The core element of the proposed method is a deeplearning process which enables a helicopter landing site analysis through the identification of soccer fields. The method trains a deep learning autoencoder with the help of volunteered geographic information and satellite images. The process is mostly automated, it was developed to be applied in a time- and resource-constrained environment and keeps the human factor in the loop in order to control the final decisions. We show that through this process the cognitive load (CL) for an expert image analyst will be reduced by 70%, while the process will successfully identify 85.6% of the potential landing sites. We conclude that the suggested methodology can be used as part of a disaster response process.
Anomaly detection in photovoltaic (PV) systems is essential to improving reliability, ensuring electricity production and equipment safety, and decreasing their negative impact on the economy of the operation system. ...
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Anomaly detection in photovoltaic (PV) systems is essential to improving reliability, ensuring electricity production and equipment safety, and decreasing their negative impact on the economy of the operation system. In many real-world scenarios—such as limited historical data, incomplete documentation, varying conditions, data corruption, or privacy issues—insufficient and unlabelled data challenge traditional anomaly detection and supervised learning methods for PV systems. Therefore, this paper proposes an effective unsupervised data anomaly detection model based on a deep neural network autoencoder. This model does not require prior knowledge about the system and accurately identifies PV system anomalies with limited information. The proposed model only uses measured PV power production as input and does not need additional information on PV system parameters or measurement data. Additionally, we derived an optimal threshold to detect anomalies based on the mean and standard deviation of the reconstruction error, resulting in a significant improvement in the F1-score from 0.9123 with the traditional approach to 0.9993. Lastly, a novel locally adaptive mechanism based on Dynamic Time Warping (DTW) error analysis is proposed to effectively locate anomaly segments by considering the shape of anomalous parts within the input time series data. The proposed model is validated on a real PV power plant in Genoa, Italy. The case study results demonstrate that the model outperforms other unsupervised machine learning models with a 0.9535 F1-score in testing and shows performance comparable to that of advanced supervised models, including XGBoost and deep neural networks.
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