Audio and device feature fusion for load recognition

Zoha, A., Gluhak, A., Nati, M., Imran, M. A. and Rajasegarar, S. (2016) Audio and device feature fusion for load recognition. In: Novel Applications of Intelligent Systems. Series: Studies in computational intelligence (586). Springer Berlin Heidelberg, pp. 287-300. ISBN 9783319141930 (doi:10.1007/978-3-319-14194-7_15)

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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.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad
Authors: Zoha, A., Gluhak, A., Nati, M., Imran, M. A., and Rajasegarar, S.
College/School:College of Science and Engineering > School of Engineering
Publisher:Springer Berlin Heidelberg
ISBN:9783319141930

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