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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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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