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

Matsukawa Shun | Zendy; Nakayama Takuya | Zendy; Ninagawa Chuzo | Zendy; Morikawa Junji | Zendy

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

Author(s) -

Matsukawa Shun,

Nakayama Takuya,

Ninagawa Chuzo,

Morikawa Junji

Publication year - 2019

Publication title -

ieej transactions on electrical and electronic engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.254

H-Index - 30

eISSN - 1931-4981

pISSN - 1931-4973

DOI - 10.1002/tee.22997

Subject(s) - air conditioning , artificial neural network , computer science , smart grid , control (management) , power consumption , electricity , training (meteorology) , real time computing , power demand , power grid , power (physics) , automotive engineering , engineering , artificial intelligence , electrical engineering , mechanical engineering , physics , quantum mechanics , meteorology

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.

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