Abstract
Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multi-layer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J. Uteley, L. Shorrock, Domestic Energy Fact File 2008. Technical Representative (Building Research Establishment, Garston, UK, 2008)
G.W. Hart, Nonintrusive appliance load monitoring. IEEE Proc. 80(12), 1870–1891 (1992)
A.I. Cole, A. Albicki, Data extraction for effective non-intrusive identification of residential power loads, in In Proceedings of Instrumentation and Measurement Technology Conference (IMTC’10), vol. 2 (St. Paul, MN, USA, 1998), pp. 812–815
Y. Du, L. Du, B. Lu, R. Harley, T. Habetler, A review of identification and monitoring methods for electric loads in commercial and residential buildings, in In Proceedings of IEEE Energy Conversion Congress and Exposition (ECCE) (Atlanta,USA, 2010), pp. 4527–4533
S.R. Shaw, S.B. Leeb, L.K. Norford, R.W. Cox, Nonintrusive load monitoring and diagnostics in power systems. IEEE Trans. Instrum. Meas. 57(7), 1445–1454 (2008)
M. Hazas, A. Friday, J. Scott, Look back before leaping forward: four decades of domestic energy inquiry. IEEE Pervas. Comput. 10(1), 13–19 (2011)
L.K. Norford, S.B. Leeb, Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energ. Build. 24(1), 51–64 (1996)
T. Kato, H.S. Cho, D. Lee, Appliance recognition from electric current signals for information-energy integrated network in home environments, in In Proceedings of 7th International Conference on Smart Homes and Health Telematics (ICOST2009), vol. 5597. (Springer, Tours, France, 2009), pp. 150–157
A. Schoofs, A. Guerrieri, D. Delaney, G. O’Hare, A. Ruzzelli, ANNOT: automated electricity data annotation using wireless sensor networks, in In Proceedings of 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON). (Massachusetts, USA, 2010), pp. 1–9
Z.C. Taysi, M.A. Guvensan, Tinyears: spying on house appliances with audio sensor nodes, in The Proceedings of 2nd ACM Workshop on Embedded Sensing Systems for Energy Efficiency in Building (2010), pp. 31–36
M. Cowling, Non-Speech Environmental Sound Classification System for Autonomous Surveillance. Ph.D. thesis (Faculty of Engineering and Information Technology, Griffith University 2004)
C.J.C. Burges, A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 43 (1998)
A. Temko, C. Nadeu, Classification of acoustic events using svm-based clustering schemes. Pattern Recogn. 39, 682–694 (2006)
C. Nadeu, J. Hernando, M. Gorricho, On the decorrelation of filter-bank energies in speech recognition, in Proceedings of Eurospeech (1995), pp. 1381–1384
J.A. Bilmes, A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Int. Comput. Sci. Inst. 4(510), 126 (1998)
Acknowledgments
We acknowledge the support from the REDUCE project grant (no:EP/I000232/1) under the Digital Economy Programme run by Research Councils UK—a cross council initiative led by EPSRC and contributed to by AHRC, ESRC, and MRC.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Zoha, A., Gluhak, A., Nati, M., Imran, M.A., Rajasegarar, S. (2016). Acoustic and Device Feature Fusion for Load Recognition. In: Hadjiski, M., Kasabov, N., Filev, D., Jotsov, V. (eds) Novel Applications of Intelligent Systems. Studies in Computational Intelligence, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-319-14194-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-14194-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14193-0
Online ISBN: 978-3-319-14194-7
eBook Packages: EngineeringEngineering (R0)