IEEJ Transactions on Industry Applications
Online ISSN : 1348-8163
Print ISSN : 0913-6339
ISSN-L : 0913-6339
Special Issue Paper
Training Method for Smart Grid Power Limitation Prediction Model of Building Air-conditioners with FastADR Signal Modulation during Normal Operation
Chuzo NinagawaYoshifumi AokiAtsushi NakamuraJunji MorikawaSeiji KondoTakashi Inaba
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2018 Volume 138 Issue 3 Pages 199-205

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Abstract

Fast Automated Demand Response (FastADR), which controls the power consumption of customers' loads, is one of the future smart grid technologies. In this paper, a neural network modeling of the FastADR response property for the power consumption of building air-conditioners is studied. We propose an efficient training data collection method with operation condition zoning using the FastADR-like signal modulation during normal air-conditioning operation.

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© 2018 by the Institute of Electrical Engineers of Japan
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