Real-Time Energy Scheduling Applying the Twin Delayed Deep Deterministic Policy Gradient and Data Clustering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00372050" target="_blank" >RIV/68407700:21230/24:00372050 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/JSYST.2023.3326978" target="_blank" >https://doi.org/10.1109/JSYST.2023.3326978</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/JSYST.2023.3326978" target="_blank" >10.1109/JSYST.2023.3326978</a>
Alternative languages
Result language
angličtina
Original language name
Real-Time Energy Scheduling Applying the Twin Delayed Deep Deterministic Policy Gradient and Data Clustering
Original language description
Smart homes are structural parts of the smart grid, since they contain controllable devices and energy management systems. In this work, we propose a reinforcement learning (RL)-based method for the energy scheduling of a smart home's energy storage system, heating ventilation and air conditioning system, and electric vehicle (EV). The proposed method targets to jointly minimize three evaluation metrics; the smart home's electricity cost, the residents' thermal discomfort, and the EV user's range anxiety. An advanced reinforcement learning algorithm, the twin delayed deep deterministic policy gradient (TD3), is utilized for this purpose together with a process, which is based on data clustering, that augments the similarity degree between the train and the test sets. As a result, the considered evaluation metrics show a significant improvement. The smart homes electricity cost, for instance, can be reduced by up to 11.2%, when compared with other RL-based works in the existing literature.
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Systems Journal
ISSN
1932-8184
e-ISSN
1937-9234
Volume of the periodical
18
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
51-60
UT code for WoS article
001106704900001
EID of the result in the Scopus database
2-s2.0-85177065571