Optimized hybrid ensemble learning approaches applied to very short-term load forecasting

Yamasaki, Marcos; Freire, Roberto Zanetti; Seman, Laio Oriel; Stefenon, Stefano Frizzo; Mariani, Viviana Cocco; dos Santos Coelho, Leandro

doi:10.1016/j.ijepes.2023.109579

Cited by 27 publications

(4 citation statements)

References 46 publications

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“…Next, the upper and lower envelopes are created by the interpolation of the local maxima and minima, employing cubic spline interpolation. The mean of the envelopes is then calculated as follows:

where

is the upper envelope and the

is the lower envelope [ 91 ].…”

Section: Methodsmentioning

confidence: 99%

Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid

Klaar,

Seman,

Mariani

et al. 2024

Sensors

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The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.

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where

is the upper envelope and the

is the lower envelope [ 91 ].…”

Section: Methodsmentioning

confidence: 99%

Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid

Klaar,

Seman,

Mariani

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Leveraging artificial intelligence (AI) techniques emerges as an enticing approach to addressing this issue. AI, a domain within computer science, demonstrates the capacity to comprehend tasks [134]; analyze data [135]; evaluate time series [136], optimization design [137], and classification tasks [138][139][140]; and make decisions using algorithms crafted by experts [141].…”

Section: Artificial Intelligence Applicationsmentioning

confidence: 99%

“…In [156], the wavelet transform was applied to improve the ability to predict faults in the electrical power system. The use of filters, such as in [157] and in [158] using seasonal trend decomposition, in [159] using wavelet transform, in [160] using Christiano-Fitzgerald random walk filter, and in [161] based on Hodrick-Prescott filter, is becoming popular since they reduce the noise and enhance the ability of the neural network to make predictions [136].…”

Section: Artificial Intelligence Applicationsmentioning

confidence: 99%

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

Buratto,

Muniz,

Nied

et al. 2024

Sensors

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This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

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“…In this field, several authors are working to reduce the complexity of the classification step, considering the use of big data [41][42][43] and making this evaluation more efficient [44][45][46]. Finally, correlation-based methods compare features by similarity to determine whether they contain Duplication, often using a final step to eliminate a portion of the found correlations.…”

mentioning

confidence: 99%

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

Alencar,

Lopes,

Fernandes

et al. 2024

Future Internet

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In the current era of social media, the proliferation of images sourced from unreliable origins underscores the pressing need for robust methods to detect forged content, particularly amidst the rapid evolution of image manipulation technologies. Existing literature delineates two primary approaches to image manipulation detection: active and passive. Active techniques intervene preemptively, embedding structures into images to facilitate subsequent authenticity verification, whereas passive methods analyze image content for traces of manipulation. This study presents a novel solution to image manipulation detection by leveraging a multi-stream neural network architecture. Our approach harnesses three convolutional neural networks (CNNs) operating on distinct data streams extracted from the original image. We have developed a solution based on two passive detection methodologies. The system utilizes two separate streams to extract specific data subsets, while a third stream processes the unaltered image. Each net independently processes its respective data stream, capturing diverse facets of the image. The outputs from these nets are then fused through concatenation to ascertain whether the image has undergone manipulation, yielding a comprehensive detection framework surpassing the efficacy of its constituent methods. Our work introduces a unique dataset derived from the fusion of four publicly available datasets, featuring organically manipulated images that closely resemble real-world scenarios. This dataset offers a more authentic representation than other state-of-the-art methods that use algorithmically generated datasets based on image patches. By encompassing genuine manipulation scenarios, our dataset enhances the model’s ability to generalize across varied manipulation techniques, thereby improving its performance in real-world settings. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. Moreover, our proposed approach exhibits reduced accuracy variance compared to alternative models, underscoring its stability and robustness across diverse datasets. The approach outlined in this work needs to provide information about the specific location or type of tempering, which limits its practical applications.

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Optimized hybrid ensemble learning approaches applied to very short-term load forecasting

Cited by 27 publications

References 46 publications

Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid

Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks

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