scholarly journals Asymmetric Confidentiality in Blockchain Embedded Smart Grids in Galois Field

2021 ◽  
Vol 4 ◽  
Author(s):  
Bannishikha Banerjee ◽  
Ashish Jani ◽  
Niraj Shah
Keyword(s):  
Smart Grid ◽  
Private Information ◽  
Smart Grids ◽  
Gradient Descent ◽  
Galois Field ◽  
Stochastic Gradient Descent ◽  
Quantum Computers ◽  
Public Key Cryptosystem ◽  
Electricity Bill ◽  
Consumption Data

Economic growth requires a sharp increase in the utilization of energy. Since the initial mechanical era, financial development has been driven by industrialization, transportation, and, most important of all, electrification, majorly achieved by petroleum product ignition. This way of development has had malicious and abusive aftershocks on the environment since the beginning. Smart grids are an idea to slightly diminish the burden on our Mother Nature, but this idea is getting tainted by the anticipation of ferocious technophiles who may try to get the grid down using quantum computers in the coming years. Thus, security becomes one of the major concerns for the smart grid. In this paper, we propose a quantum-resistant framework for associating smart grids and blockchain embedded with a permutation-substitution-based public-key cryptosystem in Galois Field to prevent unauthorized access and perform encryption of the private information of the user and consumption statistics. Permutation and substitution are performed to increase the diffusion and confusion of the data. Expenditures are quantified from the dissipation particulars, and the payment of electricity bill is performed using our blockchain wallet. The prediction model of consumption data is generated availing stochastic gradient descent. The performance analysis of the proposed cryptosystem is predicted after a simulation of the smart grid.

scholarly journals A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction

2021 ◽  
Author(s):  
ANKIT GHOSH ◽  
ALOK KOLE
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Smart Grids ◽  
Machine Learning Algorithms ◽  
Electricity Sector ◽  
Stochastic Gradient Descent ◽  
Energy Output ◽  
Gradient Boosting ◽  
Support Vector

<p>Smart grid is an essential concept in the transformation of the electricity sector into an intelligent digitalized energy network that can deliver optimal energy from the source to the consumers. Smart grids being self-sufficient systems are constructed through the integration of information, telecommunication, and advanced power technologies with the existing electricity systems. Artificial Intelligence (AI) is an important technology driver in smart grids. The application of AI techniques in smart grid is becoming more apparent because the traditional modelling optimization and control techniques have their own limitations. Machine Learning (ML) being a sub-set of AI enables intelligent decision-making and response to sudden changes in the customer energy demands, unexpected disruption of power supply, sudden variations in renewable energy output or any other catastrophic events in a smart grid. This paper presents the comparison among some of the state-of-the-art ML algorithms for predicting smart grid stability. The dataset that has been selected contains results from simulations of smart grid stability. Enhanced ML algorithms such as Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD) classifier, XGBoost and Gradient Boosting classifiers have been implemented to forecast smart grid stability. A comparative analysis among the different ML models has been performed based on the following evaluation metrics such as accuracy, precision, recall, F1-score, AUC-ROC, and AUC-PR curves. The test results that have been obtained have been quite promising with the XGBoost classifier outperforming all the other models with an accuracy of 97.5%, recall of 98.4%, precision of 97.6%, F1-score of 97.9%, AUC-ROC of 99.8% and AUC-PR of 99.9%. </p>

scholarly journals A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction

2021 ◽  
Author(s):  
ANKIT GHOSH ◽  
ALOK KOLE
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Smart Grids ◽  
Machine Learning Algorithms ◽  
Electricity Sector ◽  
Stochastic Gradient Descent ◽  
Energy Output ◽  
Gradient Boosting ◽  
Support Vector

<p>Smart grid is an essential concept in the transformation of the electricity sector into an intelligent digitalized energy network that can deliver optimal energy from the source to the consumers. Smart grids being self-sufficient systems are constructed through the integration of information, telecommunication, and advanced power technologies with the existing electricity systems. Artificial Intelligence (AI) is an important technology driver in smart grids. The application of AI techniques in smart grid is becoming more apparent because the traditional modelling optimization and control techniques have their own limitations. Machine Learning (ML) being a sub-set of AI enables intelligent decision-making and response to sudden changes in the customer energy demands, unexpected disruption of power supply, sudden variations in renewable energy output or any other catastrophic events in a smart grid. This paper presents the comparison among some of the state-of-the-art ML algorithms for predicting smart grid stability. The dataset that has been selected contains results from simulations of smart grid stability. Enhanced ML algorithms such as Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD) classifier, XGBoost and Gradient Boosting classifiers have been implemented to forecast smart grid stability. A comparative analysis among the different ML models has been performed based on the following evaluation metrics such as accuracy, precision, recall, F1-score, AUC-ROC, and AUC-PR curves. The test results that have been obtained have been quite promising with the XGBoost classifier outperforming all the other models with an accuracy of 97.5%, recall of 98.4%, precision of 97.6%, F1-score of 97.9%, AUC-ROC of 99.8% and AUC-PR of 99.9%. </p>

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Sistemas de Comunicaciones y Redes de Datos en Smart Grids, una Revisión al Estado del Arte

2016 ◽  
Vol 12 (1) ◽  
Author(s):  
R. Herrera ◽  
L. Herrera
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
La Red

Las smart grids han sido concebidas como la combinación de la red eléctrica tradicional (generación, transmisión, distribución, y comercialización, incluyendo las energías alternativas) con las redes de comunicaciones electrónicas. Este concepto revoluciona la administración, supervisión, y mantenimiento de la red eléctrica, volviéndola inteligente ante sobrecargas, caídas, apagones, caídas de tensión disminuyendo los tiempos de respuesta ante estos problemas. En este trabajo se analizan las tecnologías de redes de datos y comunicaciones electrónicas implicadas en este nuevo concepto de gestión eficiente de la electricidad. En la primera sección se abordan conceptos introductorios para entender las diferencias entre las redes eléctricas tradicionales y las smart grids, luego se realiza un análisis de las arquitecturas y requerimientos de diseño de una smart grid, para en la siguiente sección elaborar una revisión de las tecnologías de comunicaciones actualmente usadas en smart grids, para finalmente analizar los retos de diseño, líneas de investigación y estandarización actuales en las tecnologías de smart grids. En la última sección se anexan las conclusiones de la realización de este trabajo.

Economic and environmental aspects of Smart Grid technologies implementation in Ukraine

2020 ◽  
pp. 28-37
Author(s):  
Oleksandra V. Kubatko ◽  
Diana O. Yaryomenko ◽  
Mykola O. Kharchenko ◽  
Ismail Y. A. Almashaqbeh
Keyword(s):  
Smart Grid ◽  
Environmental Sustainability ◽  
Distribution System ◽  
Energy Security ◽  
Smart Grids ◽  
Renewable Energy Sources ◽  
Average Energy ◽  
Time Of Day ◽  
Electricity Supply ◽  
Auxiliary Equipment

Interruptions in electricity supply may have a series of failures that can affect banking, telecommunications, traffic, and safety sectors. Due to the two-way interactive abilities, Smart Grid allows consumers to automatically redirect on failure, or shut down of the equipment. Smart Grid technologies are the costly ones; however, due to the mitigation of possible problems, they are economically sound. Smart grids can't operate without smart meters, which may easily transmit real-time power consumption data to energy data centers, helping the consumer to make effective decisions about how much energy to use and at what time of day. Smart Grid meters do allow the consumer to track and reduce energy consumption bills during peak hours and increase the corresponding consumption during minimum hours. At a higher level of management (e.g., on the level of separate region or country), the Smart Grid distribution system operators have the opportunity to increase the reliability of power supply primarily by detecting or preventing emergencies. Ukraine's energy system is currently outdated and cannot withstand current loads. High levels of wear of the main and auxiliary equipment of the power system and uneven load distribution in the network often lead to emergencies and power outages. The Smart Grid achievements and energy sustainability are also related to the energy trilemma, which consists of key core dimensions– Energy Security, Energy Equity, and Environmental Sustainability. To be competitive in the world energy market, the country has to organize efficiently the cooperation of public/private actors, governments, economic and social agents, environmental issues, and individual consumer behaviors. Ukraine gained 61 positions out of 128 countries in a list in 2019 on the energy trilemma index. In general, Ukraine has a higher than average energy security position and lower than average energy equity, and environmental sustainability positions. Given the fact that the number of renewable energy sources is measured in hundreds and thousands, network management is complicated and requires a Smart Grid rapid response. Keywords: economic development, Smart Grid, electricity supply, economic and environmental efficiency.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

Global Trends in the Political Economy of Smart Grids

2017 ◽  
Author(s):  
Cherrelle Eid ◽  
Rudi Hakvoort ◽  
Martin de Jong
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
The United States ◽  
Industry Structure ◽  
Electricity Sector ◽  
Distributed Energy ◽  
Global Trends ◽  
Political Economic ◽  
Regulatory Models ◽  
Global Transition

The global transition towards sustainable, secure, and affordable electricity supply is driving changes in the consumption, production, and transportation of electricity. This chapter provides an overview of three main causes of political–economic tensions with smart grids in the United States, Europe, and China, namely industry structure, regulatory models, and the impact of energy policy. In all cases, the developments are motivated by the possible improvements in reliability and affordability yielded by smart grids, while sustainability of the electricity sector is not a central motivation. A holistic smart grid vision would open up possibilities for better integration of distributed energy resources. The authors recommend that smart grid investments should remain outside of the regulatory framework for utilities and distribution service operators in order to allow for such developments.

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