An online HEMS scheduling method based on deep recurrent neural network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationECOS 2019 - Proceedings of the 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
EditorsWojciech Stanek, Pawel Gladysz, Sebastian Werle, Wojciech Adamczyk
PublisherInstitute of Thermal Technology
Pages1327-1335
Number of pages9
ISBN (Electronic)9788361506515
Publication statusPublished - 2019
Event32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2019 - Wroclaw, Poland
Duration: 2019 Jun 232019 Jun 28

Publication series

NameECOS 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
CountryPoland
CityWroclaw
Period19/6/2319/6/28

Keywords

  • Encoder-decoder
  • Energy demand forecast
  • Home energy management system
  • Recurrent neural network
  • Scheduling

ASJC Scopus subject areas

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

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  • Cite this

    Yoshida, A., Fujimoto, Y., Amano, Y., & Hayashi, Y. (2019). An online HEMS scheduling method based on deep recurrent neural network. In W. Stanek, P. Gladysz, S. Werle, & W. Adamczyk (Eds.), 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.