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A novel information technology of load events detection for the energy management information systems

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Abstract

Coordinating economic load demand response (ELDR) strategy with energy efficiency and information technology (IT) of e-business management multiplies the reduction in electricity usage. The steady-state power signatures (PS) contain plenty of information needed for detecting state transition and aging of loads. On the other hand, adopting the values of PS directly has the drawbacks of taking a longer time and much memory for the datasets of energy management information system (EMIS). To effectively reduce the number of PS representing load state transition and aging signals, a feature extraction technique of the PS in the EMIS, Hellinger distance, is proposed in this paper. The high success rates of identifying state transition and aging of loads from the back-propagation artificial neural network (BP-ANN) have been proved via experiments to be feasible in load operations of EMIS applications.

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Acknowledgments

The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC 102-2221-E-228-002.

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Authors and Affiliations

  1. Department of Electronic Engineering, Jin Wen University of Science and Technology, No. 99, Anzhong Rd., Xindian Dist., New Taipei City, 23154, Taiwan, ROC

    Hsueh-Hsien Chang

  2. Department of Electrical Engineering, Ming Hsin University of Science and Technology, Hsin-Chu, Taiwan, ROC

    Ching-Lung Lin

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  1. Hsueh-Hsien Chang
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Correspondence to Hsueh-Hsien Chang.

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Chang, HH., Lin, CL. A novel information technology of load events detection for the energy management information systems. Inf Syst E-Bus Manage 13, 289–308 (2015). https://doi.org/10.1007/s10257-014-0261-4

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  • DOI: https://doi.org/10.1007/s10257-014-0261-4

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