Abstract
This study proposes a Hellinger distance algorithm for extracting the power features of aging load based on a non-intrusive load monitoring system (NILM). Hellinger distance algorithm is used to extract optimal features for load identification and the back-propagation artificial neural network (BP-ANN) is employed for the aging load detection. The proposed methods are used to analyze and identify the load characteristics and aging load in residential building. The result of aging load detection can provide the demand information for each load. The recognition result shows that the accuracy can be improved by using the proposed feature extraction method. In order to reduce the consumption of energy and send a real-time alarm of aging load to the user, the system provides the information of energy usage from the data analyses.
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© 2014 Springer International Publishing Switzerland
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Chang, HH., Lee, MC., Chen, N. (2014). A Novel Method for Extracting Aging Load and Analyzing Load Characteristics in Residential Buildings. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8482. Springer, Cham. https://doi.org/10.1007/978-3-319-07467-2_20
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DOI: https://doi.org/10.1007/978-3-319-07467-2_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07466-5
Online ISBN: 978-3-319-07467-2
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