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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

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

2022

Kód důvěrnosti údajů

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

Název periodika

IEEE Internet of Things Journal

ISSN

2327-4662

e-ISSN

2327-4662

Svazek periodika

9

Číslo periodika v rámci svazku

17

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

16363-16371

Kód UT WoS článku

000846738200074

EID výsledku v databázi Scopus

2-s2.0-85125357587