Neural Network with Extended Input for Estimating Electricity Consumption Using Background-based Data Generation-Reference-Cited by-同舟云学术

Neural Network with Extended Input for Estimating Electricity Consumption Using Background-based Data Generation

Published:2019-02 Issue: Volume:158 Page:2683-2688
ISSN:1876-6102
Container-title:Energy Procedia
language:en
Short-container-title:Energy Procedia

Author:

Cui Gaochen,Liu Bo,Luan Wenpeng

Publisher

Elsevier BV

Reference14 articles.

1. S. Darby. The effectiveness of feedback on energy consumption. A review for Defra of the literature on metering, billing and direct displays. Environmental Change Inst., Univ. Oxford, Oxford, U.K., Tech. Rep., 2006.

2. S. B. Leeb, S. R. Shaw, J. L. Kirtley, Jr. Transient Event Detection in Spectral Envelope Estimates for Nonintrusive Load Monitoring. IEEE Trans. Power Delivery, p. 1200, Jul. 1995.

3. N. Amirach, B. Xerri, B. Borloz and C. Jauffret. A new approach for event detection and feature extraction for NILM. 2014 21st IEEE ICECS., Marseille, 2014, pp. 287–290.

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