Support-Vector-Machine-Based Proactive Cascade Prediction in Smart Grid Using Probabilistic Framework

Gupta, Sandeep K.; Kambli, Ruta; Wagh, Sushama; Kazi, Faruk

doi:10.1109/tie.2014.2361493

Cited by 128 publications

(35 citation statements)

References 29 publications

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“…SVM has been widely adopted to address the issues in product feature design, fault detection, forecasting, clustering and pattern recognition across the application domains such as manufacturing, smart grid, transportation as well as smart home due to its maturity and transparency. The method can take different sizes of input data to carry out the classification and regression, so it has been used in the applications that require short response time such as [85] [86]. It also used in conjunction with other machine learning methods such as ANN, and Bayesian etc.…”

Section: Machine Learning Methods In Cpsmentioning

confidence: 99%

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Fei

Shah

Verba

et al. 2019

Future Generation Computer Systems

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Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture.

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Section: Machine Learning Methods In Cpsmentioning

confidence: 99%

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Fei

Shah

Verba

et al. 2019

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…Taking advantage of the computational efficiency of SVMs in comparison to stochastic models such as the Markov model, the authors of Reference [46] propose an SVM-based blackout prediction system for smart grid applications. By inputting past data and the real-time status of the transmissions lines (e.g., load distribution, mean, variance, and cumulative distribution function of power), SVM algorithm classifies grid's status into either normal or blackout.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Smart City System Design

et al. 2019

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Recent global smart city efforts resemble the establishment of electricity networks when electricity was first invented, which meant the start of a new era to sell electricity as a utility. A century later, in the smart era, the network to deliver services goes far beyond a single entity like electricity. Supplemented by a wellestablished Internet infrastructure that can run an endless number of applications, abundant processing and storage capabilities of clouds, resilient edge computing, and sophisticated data analysis like machine learning and deep learning, an already-booming Internet of Things movement makes this new era far more exciting. In this article, we present a multi-faceted survey of machine intelligence in modern implementations. We partition smart city infrastructure into application, sensing, communication, security, and data planes and put an emphasis on the data plane as the mainstay of computing and data storage. We investigate (i) a centralized and distributed implementation of data plane's physical infrastructure and (ii) a complementary application of data analytics, machine learning, deep learning, and data visualization to implement robust machine intelligence in a smart city software core. We finalize our article with pointers to open issues and challenges.

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“…At long last their result demonstrates that the vitality utilization can be to a great extent approximated with a Gaussian distribution and the SVM based machine learning methodologies could precisely determining the vitality use. Sudha Gupta et al [10] support Vector Machine Based Proactive Cascade Prediction in Smart Grid Using Probabilistic Framework. The principle commitment of the examination was to catch the pith of the falling disappointment utilizing probabilistic structure and combination of SVM machine learning tool.…”

Section: Literature Reviewmentioning

confidence: 99%

Survey on Machine Learning based Electric Consumption Forecasting using Smart Meter Data

Zemene¹,

Khedkar²

2017

IJCA

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The use of smart meter in electric power consumption plays great roll benefiting customer to control and manage their electric power usage. It creates smooth communication to build fair electric power distribution for customers and better management of whole electric system for suppliers. Machine learning predictive frameworks have been worked in order to utilize the electric energy assets effectively, productively and acknowledgment of advanced energy generation, circulation and utilization. This paper presents outline of research works identified with machine learning based forecasting of customers electric power utilization from smart meter data. The paper concentrates on exhaustive study of strategies and relative examination of classifier models utilized as a part of determining customer electric power consumption. Moreover, limitations, difficulties, points of interest and disadvantage of the past works identified with machine learning based methods determining of customers electric power consumption are over viewed.

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Support-Vector-Machine-Based Proactive Cascade Prediction in Smart Grid Using Probabilistic Framework

Cited by 128 publications

References 29 publications

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Smart City System Design

Survey on Machine Learning based Electric Consumption Forecasting using Smart Meter Data

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