A fuzzy TOPSIS approach for home energy management in smart grid with considering householders' preferences

It is expected that demand response programs will be designed to decrease electricity consumption or shift it from on-peak to off-peak periods depending on consumers' preferences and lifestyles. This paper demonstrates a fuzzy TOPSIS decision-making approach to quantify and evaluate consumers' preferences at the micro-level when using electrical devices according to a real-time price scheme of demand response in order to best manage the use of appliances. This enables and supports householders to maximize their participation in demand response programs.

[1]  Alessandro Di Giorgio,et al.  An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management , 2012 .

[2]  Vilja Varho,et al.  Consumers in the green electricity market in Finland , 2006 .

[3]  Paulo Carreira,et al.  The case for a systematic development of Building Automation Systems , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[4]  Keibun Mori,et al.  Modeling the impact of a carbon tax: A trial analysis for Washington State , 2012 .

[5]  Zhong Fan,et al.  An integer linear programming based optimization for home demand-side management in smart grid , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[6]  Hisham Zerriffi,et al.  Three dimensional energy profile , 2011 .

[7]  Hartmut Schmeck,et al.  User behavior prediction for energy management in smart homes , 2011, 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[8]  Sarah C. Darby,et al.  Social implications of residential demand response in cool temperate climates , 2012 .

[9]  Robert Schober,et al.  Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[10]  Omid Ameri Sianaki,et al.  A Knapsack problem approach for achieving efficient energy consumption in smart grid for endusers' life style , 2010, 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply.

[11]  Michael Nye,et al.  Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors , 2010 .

[12]  Lin Gan,et al.  Green electricity market development: Lessons from Europe and the US , 2007 .

[13]  M. Parsa Moghaddam,et al.  Modeling and prioritizing demand response programs in power markets , 2010 .

[14]  E. Plug,et al.  Similarity in response behavior between household members: An application to income evaluation , 1998 .

[15]  Lingfeng Wang,et al.  Demand response simulation implementing heuristic optimization for home energy management , 2010, North American Power Symposium 2010.

[16]  Georgios B. Giannakis,et al.  Cooperative multi-residence demand response scheduling , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[17]  Alexander L. Davis,et al.  Preparing for smart grid technologies: A behavioral decision research approach to understanding consumer expectations about smart meters , 2012 .

[18]  Yasuhiro Fuwa,et al.  Decision-making in electrical appliance use in the home , 2008 .

[19]  Limin Shi,et al.  An interval multiple attribute decision-making model based on TOPSIS and it's application in smart grid evaluation , 2011, 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC).

[20]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[21]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[22]  Chen Wang,et al.  Managing end-user preferences in the smart grid , 2010, e-Energy.

[23]  M. Newborough,et al.  Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design , 2003 .

[24]  Guy R. Newsham,et al.  The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review , 2010 .

[25]  Ashwani Kumar,et al.  Demand response by dynamic demand control using frequency linked real‐time prices , 2010 .

[26]  Omid Ameri Sianaki,et al.  Intelligent Decision Support System for Including Consumers' Preferences in Residential Energy Consumption in Smart Grid , 2010, 2010 Second International Conference on Computational Intelligence, Modelling and Simulation.

[27]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[28]  Yigzaw G. Yohanis,et al.  Domestic energy use and householders' energy behaviour , 2012 .

[29]  H. Zerriffi,et al.  Three dimensional energy profile : A conceptual framework for assessing household energy use , 2011 .

[30]  Shahin Nazarian,et al.  Concurrent optimization of consumer's electrical energy bill and producer's power generation cost under a dynamic pricing model , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[31]  Saifur Rahman,et al.  An Algorithm for Intelligent Home Energy Management and Demand Response Analysis , 2012, IEEE Transactions on Smart Grid.

[32]  Ioannis Lampropoulos,et al.  A methodology for modeling the behavior of electricity prosumers within the smart grid , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[33]  Michael Nye,et al.  Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term , 2013 .

[34]  Hong Zhang,et al.  The evaluation of tourism destination competitiveness by TOPSIS & information entropy – A case in the Yangtze River Delta of China , 2011 .

[35]  Vincent W. S. Wong,et al.  Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[36]  Morteza Yazdani,et al.  A state-of the-art survey of TOPSIS applications , 2012, Expert Syst. Appl..

[37]  P. Stern.  Information, Incentives, and Proenvironmental Consumer Behavior , 1999 .