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A Novel Method for Extracting Aging Load and Analyzing Load Characteristics in Residential Buildings

  • Hsueh-Hsien Chang
  • Meng-Chien Lee
  • Nanming Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8482)

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.

Keywords

Non-intrusive load monitoring system (NILM) aging load detection Hellinger distance back-propagation artificial neural network (BP-ANN) 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hsueh-Hsien Chang
    • 1
  • Meng-Chien Lee
    • 2
  • Nanming Chen
    • 2
  1. 1.Department of Electric EngineeringJinwen University of Science and TechnologyNew TaipeiTaiwan
  2. 2.Department of Electrical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan

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