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Added by
Dr. Nadeem Javaid
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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A significant amount of research has been conducted in order to make home appliances more efficient in terms of energy usage. Various techniques have been designed and implemented in order to control the power demand and supply. This paper encompasses reviews of different research works on a wide range of energy management techniques for smart homes aimed at reducing energy consumption and minimizing energy wastage. The idea of smart home is elaborated followed by a review of existing energy management methods.
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In this paper we present a systematic review of various home energy management (HEM) schemes. Employment of home energy management programs will make the electricity consumption smarter and more efficient. Advantages of HEM include, increased savings for consumers as well as utilities, reduced peak to average ratio (PAR) and peak demand. Where there are numerous applications of smart grid technologies, home energy management is probably the most important one to be addressed. Utilities across the globe have taken various steps for efficient consumption of electricity. New pricing schemes like, Real Time Pricing (RTP), Time of Use (ToU), Inclining Block Rates (IBR), Critical Peak Pricing (CPP) etc, have been proposed for smart grid. Distributed Energy Resources (DER) (local generation) and/or home appliances coordination along with different tariff schemes lead towards efficient consumption of electricity. This work also discusses a HEM systems general architecture and various challenges in implementation of this architecture in smart grid.
We present a detailed review of various Home Energy Management Schemes (HEM,s). HEM,s will increase savings, reduce peak demand and Pto Average Ratio (PAR). Among various applications of smart grid technologies, home energy management is probably the most important one to be addressed. Various steps have been taken by utilities for efficient energy consumption.New pricing schemes like Time of Use (ToU), Real Time Pricing (RTP), Critical Peak Pricing (CPP), Inclining Block Rates (IBR) etc have been been devised for future smart grids.Home appliances and/or distributed energy resources coordination (Local Generation) along with different pricing schemes leads towards efficient energy consumption. This paper addresses various communication and optimization based residential energy management schemes and different communication and networking technologies involved in these schemes.
Smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. In this paper, we present PINGPONG, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PINGPONG on popular smart home devices ranging from smart plugs and thermostats to cameras, voice-activated devices, and smart TVs. We were able to: (1) automatically extract previously unknown signatures that consist of simple sequences of packet lengths and directions; (2) use those signatures to detect the devices or specific events with an average recall of more than 97%; (3) show that the signatures are unique among hundreds of millions of packets of real world network traffic; (4) show that our methodology is also applicable to publicly available datasets; and (5) demonstrate its robustness in different settings: events triggered by local and remote smartphones, as well as by homeautomation systems.
Nowadays the emerging smart grid technology opens up the possibility of two-way communication between customers and energy utilities. Demand Response Management (DRM) offers the promise of saving money for commercial customers and households while helps utilities operate more efficiently. In this paper, an Incentive-based Demand Response Optimization (IDRO) model is proposed to efficiently schedule household appliances for minimum usage during peak hours. The proposed method is a multi-objective optimization technique based on Nonlinear Auto-Regressive Neural Network (NAR-NN) which considers energy provided by the utility and rooftop installed photovoltaic (PV) system. The proposed method is tested and verified using 300 case studies (household). Data analysis for a period of one year shows a noticeable improvement in power factor and customers bill.
Cyber-physical systems integrate information and communication technology functions to the physical elements of a system for monitoring and controlling purposes. The conversion of traditional power grid into a smart grid, a fundamental example of a cyber-physical system, raises a number of issues that require novel methods and applications. In this context, an important issue is the verification of certain quantitative properties of the system. In this technical report, we consider a specific Chinese Smart Grid implementation and try to address the verification problem for certain quantitative properties including performance and battery consumption. We employ stochastic model checking approach and present our modelling and analysis study using PRISM model checker.
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