Dynamic SMOTE training of neural networks used in real‐time pricing control for building air‐conditioners

Dynamic SMOTE training of neural networks used in real‐time pricing control for building air‐conditioners

Recently, smart grid Real‐Time Pricing (RTP), which changes electricity unit price for every several 10 min, has been receiving attention. The RTP control system uses a Neural Network (NN) prediction model on the response of power consumption against the power limitation commands for power saving. I...

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Veröffentlicht in:IEEJ transactions on electrical and electronic engineering 2019-11, Vol.14 (11), p.1727-1728
Hauptverfasser: Matsukawa, Shun, Nakayama, Takuya, Ninagawa, Chuzo, Morikawa, Junji
Format: Artikel
Sprache:eng
Schlagworte:
Air conditioners
Electricity pricing
Energy conservation
Neural Networks
Power consumption
Smart grid
Training
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Zusammenfassung:Recently, smart grid Real‐Time Pricing (RTP), which changes electricity unit price for every several 10 min, has been receiving attention. The RTP control system uses a Neural Network (NN) prediction model on the response of power consumption against the power limitation commands for power saving. In this research, we propose a new NN training method using the data that are observed during short‐term. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
ISSN:1931-4973
1931-4981
DOI:10.1002/tee.22997