Energy monitoring in residential spaces with audio sensor nodes: TinyEARS

Awareness on how and where energy is consumed is being increasingly recognized as the key to prevent waste in next-generation smart buildings. However, while several solutions exist to monitor energy consumption patterns for commercial and industrial users, energy reporting systems currently available to residential users require time-consuming and intrusive installation procedures, or are otherwise unable to provide device-level reports on energy consumption. To fill this gap, this paper discusses the design and performance evaluation of the Tiny Energy Accounting and Reporting System (TinyEARS), an energy monitoring system that generates device-level power consumption reports primarily based on the acoustic signatures of household appliances detected by wireless sensors. Experiments demonstrate that TinyEARS is able to report the power consumption of individual household appliances within a 10% error margin.

[1]  Gregory M. P. O'Hare,et al.  Evaluation of energy-efficiency in lighting systems using sensor networks , 2009, BuildSys '09.

[2]  Öznur Özkasap,et al.  Ad-Hoc Networks , 2008, Encyclopedia of Algorithms.

[3]  Antonio Guerrieri,et al.  ANNOT: Automated Electricity Data Annotation Using Wireless Sensor Networks , 2010, 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).

[4]  Anthony Rowe,et al.  Contactless sensing of appliance state transitions through variations in electromagnetic fields , 2010, BuildSys '10.

[5]  Mani B. Srivastava,et al.  Challenges in resource monitoring for residential spaces , 2009, BuildSys '09.

[6]  Douglas D. O'Shaughnessy,et al.  Compensated mel frequency cepstrum coefficients , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[7]  Kamin Whitehouse,et al.  The self-programming thermostat: optimizing setback schedules based on home occupancy patterns , 2009, BuildSys '09.

[8]  H. Y. Lam,et al.  A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof , 2007, IEEE Transactions on Consumer Electronics.

[9]  Michael A. Cowling,et al.  Non-Speech Environmental Sound Classification System for Autonomous Surveillance , 2004 .

[10]  Mani B. Srivastava,et al.  ViridiScope: design and implementation of a fine grained power monitoring system for homes , 2009, UbiComp.

[11]  G.W. Hart,et al.  Residential energy monitoring and computerized surveillance via utility power flows , 1989, IEEE Technology and Society Magazine.

[12]  Takashi Onoda,et al.  Estimation of power consumption for household electric appliances , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[13]  Antonio Capone,et al.  Home energy saving through a user profiling system based on wireless sensors , 2009, BuildSys '09.

[14]  Alan Marchiori,et al.  Using circuit-level power measurements in household energy management systems , 2009, BuildSys '09.

[15]  Steven B. Leeb,et al.  Power signature analysis , 2003 .

[16]  Tommaso Melodia,et al.  TinyEARS: spying on house appliances with audio sensor nodes , 2010, BuildSys '10.

[17]  Chen-Fang Tsai,et al.  Monitoring Appliances Sensor Data in Home Environment: Issues and Challanges , 2009, 2009 IEEE Conference on Commerce and Enterprise Computing.

[18]  Gregory D. Abowd,et al.  At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award) , 2007, UbiComp.

[19]  Shrikanth Narayanan,et al.  Environmental Sound Recognition With Time–Frequency Audio Features , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[20]  David E. Culler,et al.  Experiences with a high-fidelity wireless building energy auditing network , 2009, SenSys '09.

[21]  Takashi Onoda,et al.  Applying Kernel Based Subspace Classification to a Non-intrusive Monitoring for Household Electric Appliances , 2001, ICANN.

[22]  Z. Cihan Taysi,et al.  Environmental sound classification for recognition of house appliances , 2010, 2010 IEEE 18th Signal Processing and Communications Applications Conference.

[23]  Jhing-Fa Wang,et al.  Chip design of MFCC extraction for speech recognition , 2002, Integr..

[24]  David E. Culler,et al.  The nesC language: A holistic approach to networked embedded systems , 2003, PLDI.

[25]  Philip Levis,et al.  The nesC language: a holistic approach to networked embedded systems , 2003, SIGP.

[26]  Alberto E. Cerpa,et al.  Energy efficient building environment control strategies using real-time occupancy measurements , 2009, BuildSys '09.

[27]  Qi Han,et al.  Distributed wireless control for building energy management? , 2010, BuildSys '10.

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