Real-Time Energy Scheduling Applying the Twin Delayed Deep Deterministic Policy Gradient and Data Clustering

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Real-Time Energy Scheduling Applying the Twin Delayed Deep Deterministic Policy Gradient and Data Clustering

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Real-Time Energy Scheduling Applying the Twin Delayed Deep Deterministic Policy Gradient and Data Clustering

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

IEEE Systems Journal

ISSN

1932-8184

e-ISSN

1937-9234

Svazek periodika

18

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

51-60

Kód UT WoS článku

001106704900001

EID výsledku v databázi Scopus

2-s2.0-85177065571