scholarly journals A Smart Energy Meter Enabling Self-Demand Response of Consumers in Smart Cities of Tamil Nadu

2020 ◽  
Author(s):  
Shashank Singh ◽  
Selvan M. P.
Keyword(s):  
Demand Response ◽  
Smart Grids ◽  
Tamil Nadu ◽  
Smart Cities ◽  
Low Cost ◽  
Data Management System ◽  
Energy Meter ◽  
Indian State ◽  
Tamil Nadu State ◽  
Tariff Structure

<div>Demand response (DR) is one of the demand side management features under the paradigm of smart grids, wherein the consumers are encouraged to participate in the utility operations through active response to electricity price signals by altering their demand patterns. One impediment in employing demand response schemes in India is the fixed electricity tariff for the domestic consumers, which most of the Indian state utilities follow. Interestingly, the Tamil Nadu State Electricity Board (TNEB) follows an incremental block rate tariff for domestic consumers, which provides an opportunity for the implementation of proposed self–DR scheme. Hence firstly, this paper presents the design and development of a low-cost single-phase smart energy meter (SEM) which incorporates a TNEB tariff structure. Secondly, the development of an indigenous meter data management system (MDMS) software is attempted for the utility using open–source software tools. Finally, the idea of self-DR is introduced and emphasized through the coordinated operation of developed SEM and MDMS.</div>

scholarly journals A Smart Energy Meter Enabling Self-Demand Response of Consumers in Smart Cities of Tamil Nadu

2020 ◽  
Author(s):  
Shashank Singh ◽  
Selvan M. P.
Keyword(s):  
Demand Response ◽  
Smart Grids ◽  
Tamil Nadu ◽  
Smart Cities ◽  
Low Cost ◽  
Data Management System ◽  
Energy Meter ◽  
Indian State ◽  
Tamil Nadu State ◽  
Tariff Structure

<div>Demand response (DR) is one of the demand side management features under the paradigm of smart grids, wherein the consumers are encouraged to participate in the utility operations through active response to electricity price signals by altering their demand patterns. One impediment in employing demand response schemes in India is the fixed electricity tariff for the domestic consumers, which most of the Indian state utilities follow. Interestingly, the Tamil Nadu State Electricity Board (TNEB) follows an incremental block rate tariff for domestic consumers, which provides an opportunity for the implementation of proposed self–DR scheme. Hence firstly, this paper presents the design and development of a low-cost single-phase smart energy meter (SEM) which incorporates a TNEB tariff structure. Secondly, the development of an indigenous meter data management system (MDMS) software is attempted for the utility using open–source software tools. Finally, the idea of self-DR is introduced and emphasized through the coordinated operation of developed SEM and MDMS.</div>

scholarly journals Design and Implementation of Cost-Effective IoT Energy Meter to Monitor Energy Flow in Smart Grids

2020 ◽  
Vol 9 (1) ◽  
pp. 1584-1588
Keyword(s):  
Smart Grids ◽  
Energy Flow ◽  
Low Cost ◽  
Cost Effective ◽  
Android Application ◽  
Human Errors ◽  
Practical Applications ◽  
Energy Meter ◽  
Reduced Costs ◽  
Reducing Costs

This paper presents a device that uses the evolving IoT technology to design and implement an internet-based energy meter. This meters, being cheap and easy-to-implement solution, enables consumers to monitor the daily usage of electric power easily. This work primarily concerns the energy-monitoring aspect of IoT, along with discussing other advantages of this meter, such as its ability to overcome human errors and reducing dependency on manual labor, besides reducing costs in energy consumption. The proposed design in case 1,and case2 which are comprises a low-cost wireless network for smart energy along with an android application capable of automatically reading the unit and then sending the data automatically provides great advantages to users by allowing them to keep a track of their meter reading. This system will help users by allowing them to not only take steps to reduce power wastage but also bring down costs of consumption, along with minimizing the threat of power theft, which is incurs great losses to power companies. Experimental results of this study show that the proposed IoT meter works efficiently and has proven its potential in practical applications at substantially reduced costs.

scholarly journals Socio Economic Analysis of Broiler Farms in Perambalur District of Tamil Nadu State

2020 ◽  
pp. 112-117
Author(s):  
M. Kandeeban ◽  
S. Praveena ◽  
Raj Shravanthi
Keyword(s):  
Tamil Nadu ◽  
Low Cost ◽  
Primary Data ◽  
Variable Cost ◽  
Face To Face ◽  
Roof Material ◽  
Tamil Nadu State ◽  
The Government ◽  
The Right ◽  
Age Category

The study was conducted to identify the socioeconomic status, assess costs and returns of broiler farms in Perambalur District of Tamil Nadu state in India. The primary data were collected from 30 respondents through face to face interview during the period between November 2019 and January 2020. The results of the study revealed that most of the respondents were male belonging to old age category. Majority of the respondents were running broiler business as a main occupation and highly depended on institutional sources of finance. Major share of the respondents were spending higher amount to the input. The farmers were spending their amount for purchasing of lights, roof material, drinker, feeder etc. Government should initiate various schemes for the upliftment of poultry sector. Low cost vaccine and medicines may be provided by the government to the growers which will minimize the variable cost. If all the above suggestions are implemented by the concerned authorities, the broiler farming will move in the right direction and the farmers will get good income.

Design of a Digital Wireless Energy Meter for Residential Energy Monitoring Using Wireless Sensor Networks

2009 ◽  
Author(s):  
Michael Koplow ◽  
Andrew Redfern ◽  
John Cheng ◽  
Paul K. Wright
Keyword(s):  
Demand Response ◽  
Low Cost ◽  
Wireless Sensor ◽  
Electricity Pricing ◽  
Wireless Sensor Node ◽  
Residential Energy ◽  
Energy Monitoring ◽  
Energy Meter ◽  
Forward Prediction ◽  
Current Measuring

This paper presents a prototype application for residential energy monitoring using a digital energy meter coupled with a wireless sensor. The current measuring meter is designed to be placed in-line with household appliances. The data are then sent using a wireless sensor node to a low cost server. The system allows end users to measure energy expenditure and visualize the data in real time with a forward prediction of cost for running the appliance. The system is an enabling technology for a global architecture of dynamic electricity pricing schemes and load reduction programs, called Demand Response. The new system allows residents to become self aware about power consumption levels in their home and enables users to make informed decisions about demand response events.

scholarly journals Multivariate Statistical Network Monitoring–Sensor: An effective tool for real-time monitoring and anomaly detection in complex networks and systems

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092130
Author(s):  
Roberto Magán-Carrión ◽  
José Camacho ◽  
Gabriel Maciá-Fernández ◽  
Ángel Ruíz-Zafra
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Smart Grids ◽  
Detection System ◽  
Smart Cities ◽  
Low Cost ◽  
Health Care Systems ◽  
Network Monitoring ◽  
Network Systems ◽  
Multivariate Statistical

Technology evolves quickly. Low-cost and ready-to-connect devices are designed to provide new services and applications. Smart grids or smart health care systems are some examples of these applications. In this totally connected scenario, some security issues arise due to the large number of devices and communications. In this way, new solutions for monitoring and detecting security events are needed to address new challenges brought about by this scenario, among others, the real-time requirement allowing quick security event detection and, consequently, quick response to attacks. In this sense, Intrusion Detection Systems are widely used though their evaluation often relies on the use of predefined network datasets that limit their application in real environments. In this work, a real-time and ready-to-use tool for monitoring and detecting security events is introduced. The Multivariate Statistical Network Monitoring–Sensor is based on the Multivariate Statistical Network Monitoring methodology and provides an alternative way for evaluating Multivariate Statistical Network Monitoring–based Intrusion Detection System solutions. Experimental results based on the detection of well-known attacks in hierarchical network systems prove the suitability of this tool for complex scenarios, such as those found in smart cities or Internet of Things ecosystems.

A Study on Job Satisfaction among Tamil Nadu State Transport Corporation (TNSTC) Employees at Tiruvarur Branch

GIS Business ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. 230-242
Author(s):  
M. Ganesan, K ◽  
K. Veerakumar ◽  
N. R Vembu ◽  
Dr. M. K Durgamani ◽  
Dr. Renuka
Keyword(s):  
Job Satisfaction ◽  
Job Stress ◽  
Tamil Nadu ◽  
Heavy Traffic ◽  
Road Transportation ◽  
The Road ◽  
Tamil Nadu State ◽  
On The Road ◽  
Day By Day ◽  
Labour Turnover

Job satisfaction is an important factor for employees working in formal and informal sector. The job is small or big, permanent or temporary, risky or non-risky, job satisfaction is important. It is the mental feeling which drives the employees to excel. Job satisfaction is a combination of psychological, physiological and environmental circumstances. A satisfied employee is a contented and happy human being. The labour turnover depends upon job satisfaction. Even highly paid employees quit the job when they are not satisfied with the job. Road transportation in Tamilnadu is growing day by day. Job stress in the road transportation is very high due to increase in number of vehicle playing on the road and heavy traffic. The drivers and conductors working in public transport corporation are suffering from high job stress. If drivers and conductors are not satisfied with their job which leads to mental stresses and affects the productivity and also creates accidents. In this present study the researchers made an attempt to study the level of job satisfaction among the drivers and conductors who are working in the Tamilnadu State Transport Corporation (TNSTC). The study reveals the expectations of drivers and conductors working in TNSTC with regards to the attributes like salary, promotion and fringe benefits etc., are satisfactory and not detrimental. 

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

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