An online HEMS scheduling method based on deep recurrent neural network

研究成果: Conference contribution

抜粋

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.

元の言語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
出版者Institute of Thermal Technology
ページ1327-1335
ページ数9
ISBN(電子版)9788361506515
出版物ステータス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
Poland
Wroclaw
期間19/6/2319/6/28

ASJC Scopus subject areas

  • Energy(all)
  • Engineering(all)
  • Environmental Science(all)

フィンガープリント An online HEMS scheduling method based on deep recurrent neural network' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Yoshida, A., Fujimoto, Y., Amano, Y., & Hayashi, Y. (2019). An online HEMS scheduling method based on deep recurrent neural network. : W. Stanek, P. Gladysz, S. Werle, & W. Adamczyk (版), ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (pp. 1327-1335). (ECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems). Institute of Thermal Technology.