Recent developments of smart energy networks and challenges

Kannadhasan, S.; Ramalingam, Nagarajan; Jayaraman, Manjushree Kumari; Gunasekaran, Ramya

doi:10.1016/b978-0-323-91664-6.00004-8

Cited by 8 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of health sciences, the following geophysical analysis procedures are now in use: Signal Processing: ECG, EEGs, EEGs, and even functional MRI data are treated as medical signals using signal processing techniques utilized in geophysics, including filtering, noise reduction, and Fourier analysis. These methods aid in pattern recognition, abnormality quantification, and information extraction from noisy data [ 115 , 116 ]. Image Reconstruction: Medical imaging modalities like CT and MRI are developed from geophysical data inversion methods, which are used to rebuild subsurface structures.…”

Section: Geophysical Data Analysis In the Health Sciencesmentioning

confidence: 99%

“…Signal Processing: ECG, EEGs, EEGs, and even functional MRI data are treated as medical signals using signal processing techniques utilized in geophysics, including filtering, noise reduction, and Fourier analysis. These methods aid in pattern recognition, abnormality quantification, and information extraction from noisy data [ 115 , 116 ].…”

Section: Geophysical Data Analysis In the Health Sciencesmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Singh,

Nayak,

Behl

et al. 2024

Diagnostics

View full text Add to dashboard Cite

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth’s subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.

show abstract

Section: Geophysical Data Analysis In the Health Sciencesmentioning

confidence: 99%

Section: Geophysical Data Analysis In the Health Sciencesmentioning

confidence: 99%

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Singh,

Nayak,

Behl

et al. 2024

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Additionally, high-resolution data allows utilities to respond swiftly and accurately to changes in energy demand through automated adjustments during peak periods. This proactive approach helps prevent grid overload, mitigates the risk of blackouts, and reduces the reliance on expensive backup power sources (Reinhardt and Pereira, 2021;Suriyan et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Advancements in super-resolution methods for smart meter data

Iversen,

Khan,

Miraki

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

This paper presents a comprehensive review of super-resolution methods for smart meter data analysis. Smart meters provide valuable insights into household electricity consumption, but their low-frequency data limits the ability to capture detailed patterns. Super-resolution techniques address this challenge through the reconstruction of high-resolution data from low-resolution measurements. The review covers both non-machine learning-based methods (interpolation, signal processing, and statistics) and machine learning-based methods (CNNs, GANs). Four selected methods are discussed in detail, highlighting their principles, advantages, and limitations. These methods demonstrate superior accuracy in enhancing data completeness, capturing complex relationships, and improving resolution. The review contributes to the advancement of super-resolution techniques for smart meter data analysis, providing researchers and practitioners with valuable insights for efficient energy management and forecasting.

show abstract

“…It also delivers bi-directional communications between service provider corporations and users [5]. It is also possible with SMs to limit the peak usage amount of electrical energy, which can be used to terminate and remotely reconnect the electrical supply [6].…”

Section: Introductionmentioning

confidence: 99%

“…where α > 0 is the step size used to regulate the range of the random search, which should be connected to the scales of the problem of interest, and step size information may be derived by Equation (6). Entry-wise multiplicands are meant by the term "product".…”

mentioning

confidence: 99%

Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

et al. 2022

View full text Add to dashboard Cite

In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%.

show abstract

Recent developments of smart energy networks and challenges

Cited by 8 publications

References 4 publications

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

Advancements in super-resolution methods for smart meter data

Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

Contact Info

Product

Resources

About