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Smart Home's Energy Management Through a Clustering-Based Reinforcement Learning Approach

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00363750" target="_blank" >RIV/68407700:21230/22:00363750 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/JIOT.2022.3152586" target="_blank" >https://doi.org/10.1109/JIOT.2022.3152586</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/JIOT.2022.3152586" target="_blank" >10.1109/JIOT.2022.3152586</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Smart Home's Energy Management Through a Clustering-Based Reinforcement Learning Approach

  • Original language description

    Smart homes that contain renewable energy sources, storage systems, and controllable loads will be key components of the future smart grid. In this article, we develop a reinforcement-learning (RL)-based scheme for the real-time energy management of a smart home that contains a photovoltaic system, a storage device, and a heating, ventilation, and air conditioning (HVAC) system. The objective of the proposed scheme is to minimize the smart home's electricity cost and the residents' thermal discomfort by appropriately scheduling the storage device and the HVAC system on a daily basis. The problem is formulated as a Markov decision process, which is solved using the deep deterministic policy gradient (DDPG) algorithm. The main contribution of our study compared to the existing literature on RL-based energy management is the development of a clustering process that partitions the training data set into more homogeneous training subsets. Different DDPG agents are trained based on the data included in the derived subsets, while in real time, the test days are assigned to the appropriate agent, which is able to achieve more efficient energy schedules when compared to a single DDPG agent that is trained based on a unified training data set.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE Internet of Things Journal

  • ISSN

    2327-4662

  • e-ISSN

    2327-4662

  • Volume of the periodical

    9

  • Issue of the periodical within the volume

    17

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    9

  • Pages from-to

    16363-16371

  • UT code for WoS article

    000846738200074

  • EID of the result in the Scopus database

    2-s2.0-85125357587