An online HEMS scheduling method based on deep recurrent neural network

研究成果: Conference contribution

抄録

For a daily-basis scheduling of an energy system, energy management system often enough to use not a global optimal scheduling but a near optimal scheduling. The article proposes an online scheduling framework without online optimization. The framework is built from two encoder-decoder architectures to extract features of time series; a multi-layer long short-term memory regression model for multi-step time-series forecasting, and multi-class and single-label classification model for on/off scheduling of a device. The models are estimated at offline, and return scheduling from historical time series as input, at online. We evaluate the accuracy of scheduling from the viewpoint of Kullback-Leibler divergence which measures the dissimilarity between two probability distributions. Through the numerical experiments, we demonstrate the usefulness of the proposed framework.

本文言語English
ホスト出版物のタイトルECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
編集者Wojciech Stanek, Pawel Gladysz, Sebastian Werle, Wojciech Adamczyk
出版社Institute of Thermal Technology
ページ1327-1335
ページ数9
ISBN(電子版)9788361506515
出版ステータスPublished - 2019
イベント32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2019 - Wroclaw, Poland
継続期間: 2019 6 232019 6 28

出版物シリーズ

名前ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems

Conference

Conference32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2019
国/地域Poland
CityWroclaw
Period19/6/2319/6/28

ASJC Scopus subject areas

  • エネルギー(全般)
  • 工学(全般)
  • 環境科学(全般)

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