Home energy saving through a user profiling system based on wireless sensors

The high energy required by home appliances (like white goods, audio/video devices and communication equipments) and air conditioning systems (heating and cooling), makes our homes one of the most critical areas for the impact of energy consumption on natural environment. In this paper we present a work in progress within the European project AIM for the design of a system that can minimize energy waste in home environments efficiently managing devices operation modes. In our architecture we use a wireless sensor network to monitor physical parameters (like light and temperature) as well as the presence of users at home and in each of its rooms. With gathered data our system creates profiles of the behavior of house inhabitants and through a prediction algorithm is able to automatically set system parameters in order to optimize energy consumption and cost while guaranteeing the required comfort level. When users change their habits due to unpredictable events, the system is able to detect wrong predictions analyzing in real time information from sensors and to modify system behavior accordingly. By the automatic control of energy management system it is possible to avoid complex manual settings of system parameters that would prevent the introduction of home automation systems for energy saving into the mass market.

[1]  Diane J. Cook,et al.  Learning to Control a Smart Home Environment , 2003 .

[2]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[3]  K. P. Wacks,et al.  Utility load management using home automation , 1991 .

[4]  Antonio Capone,et al.  A New Architecture for Reduction of Energy Consumption of Home Appliances , 2009 .

[5]  Karen Henricksen,et al.  A survey of middleware for sensor networks: state-of-the-art and future directions , 2006, MidSens '06.

[6]  Spyridon L. Tompros,et al.  Enabling applicability of energy saving applications on the appliances of the home environment , 2009, IEEE Network.

[7]  Michael C. Mozer,et al.  The Neural Network House: An Environment that Adapts to its Inhabitants , 1998 .

[8]  Diane J. Cook,et al.  How smart are our environments? An updated look at the state of the art , 2007, Pervasive Mob. Comput..

[9]  Hani Hagras,et al.  Creating an ambient-intelligence environment using embedded agents , 2004, IEEE Intelligent Systems.

[10]  Andreas Krause,et al.  Intelligent light control using sensor networks , 2005, SenSys '05.

[11]  Vishal Garg,et al.  Smart occupancy sensors to reduce energy consumption , 2000 .

[12]  Matteo Cesana,et al.  A Reconfigurable Middleware for Dynamic Management of Heterogeneous Applications in Multi-Gateway Mobile Sensor Networks , 2009, 2009 6th IEEE Annual Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks Workshops.