Applying Reinforcement Learning Method for Real-time Energy Management

Aida Borhan Dayani, Hamed Fazlollahtabar, Roya Ahmadiahangar, Argo Rosin, Mohammad Salay Naderi, Mehdi Bagheri

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

3 Citations (Scopus)

Abstract

today energy management and optimization is a key factor to control the whole production cycle, distribution and energy consumption. Electrical energy consumption optimization involves proper modeling and prediction. Preservation of energy-efficient resources and proper consumption management is one of the most important challenges in all countries of the world. In this study, we present a decision support system for managing energy by reinforcement learning. First, a set of different energy uncertain consumption data and adopted decisions were considered in the form of fuzzy. Then, the prediction of consumption was done by the Q-learning algorithm, which is a solution to the Markov decision problem. Then the rules are presented to describe what the system implies. The proposed method is capable of working in real-time approach and handle the consumption fluctuations in learning and predicting process.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728106526
DOIs
Publication statusPublished - Jun 2019
Event19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 - Genoa, Italy
Duration: Jun 11 2019Jun 14 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019

Conference

Conference19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
CountryItaly
CityGenoa
Period6/11/196/14/19

Keywords

  • Demand
  • Energy management
  • Q-learning algorithm
  • decision support system
  • reinforcement learning

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Environmental Engineering
  • Control and Optimization

Fingerprint Dive into the research topics of 'Applying Reinforcement Learning Method for Real-time Energy Management'. Together they form a unique fingerprint.

  • Cite this

    Dayani, A. B., Fazlollahtabar, H., Ahmadiahangar, R., Rosin, A., Naderi, M. S., & Bagheri, M. (2019). Applying Reinforcement Learning Method for Real-time Energy Management. In Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 [8783766] (Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EEEIC.2019.8783766