Anish Jindal

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.

SDN-Based Data Center Energy Management System Using RES and Electric Vehicles

Cloud Computing (CC) has emerged as a leading technology for providing on-demand services such as, network access, data storage, computation to end users for smooth execution of various applications. Such services are provided over physical servers hosted by large data centers (DCs) which may be geographically located. In recent times, with an increase in service requests for various resources, DCs have expanded drastically in terms of number of servers. With such an increase in high end servers, the energy consumption of DCs has escalated many folds which may lead to additional burden on the grid. Moreover, the escalation in energy consumption of DCs has an impact on carbon footprints in the environment. Hence, the integration of renewable energy sources (RES) with DCs may ease the load of grid to a great extent. However, due to intermittent nature of RES, it is a difficult task to sustain DCs using RES. Hence, to sustain the energy consumption of DCs using RES, the penetration of electric vehicles (EVs) can be a major leap. To resolve these issues, a software defined network (SDN)-based DC energy management system using RES and EVs is designed in this paper. In the proposed scheme, a charging- discharging mechanism for penetration of EVs is formulated to cope with the intermittent nature of RES. The results obtained clearly depict that the penetration of EVs played a major role to manage the energy consumption of DC using RES.

A data analytical approach using support vector machine for demand response management in smart grid

Many existing issues pertaining to power sector such as-demand response management, theft detection, outage management etc. can be solved efficiently with grid modernization. Out of these, demand response is one such issue which affects the overall grid stability. One way of managing demand response is to balance the load in smart grid (SG). In this paper, a novel scheme for handling the demand response in SG is presented. The household load is managed in such a way that the load profile of the SG is flattened. Unlike existing approaches, the proposed scheme is based on the data analytics and works as follows. Initially, the data is gathered from all the devices and the users with excess load consumption are identified using the support vector machine (SVM). To curtail the load of such users, a novel load balancing algorithm has been designed. This algorithm sheds the excess load in homes so as to balance the overall load in SG. The simulation results show that the proposed scheme effectively flattens the load profile of SG while managing the demand response.

EnergyChain: Enabling Energy Trading for Smart Homes using Blockchains in Smart Grid Ecosystem

The amalgamation of information and communication technologies in power industry has led to a revolution known as smart grid (SG). The energy consumers interact with the power utility using a bidirectional communication channel for energy trading in SG ecosystem. However, the traditional energy trading mechanisms strongly rely on trusted third parties which act as a single point of failure. Therefore, it is important to equip SG with a decentralized and secure energy trading system which can execute contracts and handle negotiations among various trading parties. Hence, in this paper, EnergyChain, a blockchain model for storing and accessing the data generated by smart homes in a secure manner is proposed. EnergyChain works in following phases: 1) a miner node is selected on the basis of power capacity of various smart homes, 2) a block creation and validation scheme is presented, and 3) a transaction handling mechanism is designed for secure energy trading. After evaluation, the superiority of EnergyChain is validated. The results obtained show that EnergyChain outperforms the traditional scheme in terms of communication costs and computation time.

An efficient fuzzy rule-based big data analytics scheme for providing healthcare-as-a-service

With advancements in information and communication technology (ICT), there is an increase in the number of users availing remote healthcare applications. The data collected about the patients in these applications varies with respect to volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges that needs a specialized approach. To address this issue, a new fuzzy rule-based classifier for big data handling using cloud-based infrastructure is presented in this paper, with an aim to provide Healthcare-as-a-Service (HaaS) to the users located at remote locations. The proposed scheme is based upon the cluster formation using the modified Expectation-Maximization (EM) algorithm and processing of the big data on the cloud environment. Then, a fuzzy rule-based classifier is designed for an efficient decision making about the data classification in the proposed scheme. The proposed scheme is evaluated with respect to different evaluation metrics such as classification time, response time, accuracy and false positive rate. The results obtained are compared with the standard techniques to confirm the effectiveness of the proposed scheme.

EVaaS: Electric vehicle-as-a-service for energy trading in SDN-enabled smart transportation system

Abstract The increased adoption of electric vehicles (EVs) in the daily life of consumers have led towards the emergence of greener smart cities. However, the problem of energy stability, i.e., balancing the demand and supply, remains persistent in the context of charging stations (CSs). To solve this problem, a unique conceptual solution using EVs has been presented in this paper. The proposed solution deals with the problem of managing the miscellaneous power or power deficit at the CSs by utilizing EVs-as-a-service (EVaaS). On one hand, EVaaS provides opportunities to the owner of EVs to earn profit and on the other hand, it helps to balance the demand and supply at the CSs. This concept works in two steps; (1) EV-as-a-buyer: EVs act as energy buyers and CSs act as energy sellers, and (2) EV-as-a-seller: EVs act as energy sellers and CSs act as energy buyers. In EVaaS paradigm, the CSs are placed in residential, commercial, and industrial areas which broadcast their price for buying (or selling) the deficit (or excess) power from (or to) the EVs. The EVs would then decide whether to charge (or discharge) their battery power from (or at) which CSs based on the factors such as–price and distance. If both the parties come to an agreement, then the EVs would travel to the specified location and exchange the energy with CSs. For the smooth movement of EVs in the smart city, a mobility model is also designed. In addition, this approach also utilizes the software defined networking (SDN) paradigm for enabling faster communication between the entities involved. For this purpose, a flow management scheme is designed for efficient data transfer between EVs and CSs. Through this study, it has been shown that such a strategy for energy trading would help the CSs to balance their load requirements as well as provide profit to the EV owners. The results prove that SDN improves the communication in terms of delay, throughput and network utilization over the conventional networks; while EVs can be successfully utilized to manage the load requirements of various CSs to gain a significant amount of profit.