Energy Management System for Domestic Electrical Appliances

A variety of energy management systems are currently available for domestic domain, and many are concerned with real-time energy consumption monitoring and display of statistical and real time data of energy consumption. Although these systems play a crucial role in providing a detailed picture of energy consumption in home environment and contribute to influencing energy consumption behavior, households are required to then take appropriate measures to reduce energy consumption. Some energy management systems provide energy saving tips but they do not take into account households’ profiles and energy consumption of home appliances. To generate an effective and real time appliance level advice on energy consumption, the system must be able to cope with a large volume of data. The proposed system addresses this issue by taking into account household profiles and energy consumption of domestic electrical appliances. The system also uses an approach based on functional data services to deal with the challenge of processing a large volume of data in real time. DOI: 10.4018/jal.2012100104 International Journal of Applied Logistics, 3(4), 48-60, October-December 2012 49 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. emission which in turn requires reduction in use of energy generated by fossil fuel. In this paper our focus is on development of intelligent system for efficient management of household energy consumption. Household energy consumption contributes towards 27% of overall CO2 emissions in the UK (http://www. bis.gov.uk/files/file11250.pdf). The motivation is to develop an intelligent system to influence the householders’ behavior through provision of detailed information regarding their energy consumption and intelligent advice on energy efficiency measures. The proposed system actively monitors energy use in a domestic environment in real time while automatically calculating household carbon footprint. The system employs a network of energy consumption reading sensors to monitor energy consumption at appliance and device level. The intelligent energy management for home appliances was developed within the European project Digital Environment Home Energy Management System DEHEMS (http:// www.dehems.eu/). The system relies on household profiles, functional database and appliances profiles in order to provide household effective and efficient advice. One of the main functions enabled by the sensor network is appliances profiling. Appliances profiling allows discovering abnormal energy consumption of appliances by comparing their current energy consumption with their profiles. User profiling process basically includes registration process that occurs when installer installs sensor network in a house and register it with DEHEMS server. The household profiles contain information such as property type, number of occupants, and appliances being used, etc. The system connects energy consumption appliances to an information system to enable better visibility and control of energy consumption appliances. The near real-time energy consumption readings of each appliances connected to Zigbee based home area network provides households the ability to fine tune their energy consumption in various situations. The main aim of this research is to provide an effective feedback and advice on appliance level electrical power consumption to households by making their energy consumption visible and the factors that contribute towards efficient energy consumption. The paper is organized as follow. Section 2 describes the related work. In Section 3 we present a high level architecture of the system. We describe system detailed architecture in Section 4. Section 5 provides description of appliances energy consumption and their profiles. In Section 6 we describe dependencies among energy consumption activities. Sections 7 and 8 describe challenges associated with large volume of data and how to deal with these issues respectively. Section 9 is concerned with users rating on recommendation and consensus on household. Section 10 is concerned with energy saving advice knowledge base. Section 11 reports on diagnostic rules and finally Section 12 conclude the paper.