Energy management of buildings with energy storage and solar photovoltaic: A diversity in experience approach for deep reinforcement learning agents

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50021216" target="_blank" >RIV/62690094:18470/24:50021216 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S266654682300085X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S266654682300085X?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.egyai.2023.100313" target="_blank" >10.1016/j.egyai.2023.100313</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Energy management of buildings with energy storage and solar photovoltaic: A diversity in experience approach for deep reinforcement learning agents

Popis výsledku v původním jazyce

Deep reinforcement learning (DRL) is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage systems. Conventionally, DRL agents are trained by randomly selecting samples from a data set, which can result in overexposure to some data categories and under/no exposure to other data categories. Thus, the trained model may be biased towards some data groups and underperform (provide suboptimal results) for data groups to which it was less exposed. To address this issue, diversity in experience-based DRL agent training framework is proposed in this study. This approach ensures the exposure of agents to all types of data. The proposed framework is implemented in two steps. In the first step, raw data are grouped into different clusters using the K-means clustering method. The clustered data is then arranged by stacking the data of one cluster on top of another. In the second step, a selection algorithm is proposed to select data from each cluster to train the DRL agent. The frequency of selection from each cluster is in proportion to the number of data points in that cluster and therefore named the proportional selection method. To analyze the performance of the proposed approach and compare the results with the conventional random selection method, two indices are proposed in this study: the flatness index and the divergence index. The model is trained using different data sets (1-year, 3-year, and 5-year) and also with the inclusion of solar photovoltaics. The simulation results confirmed the superior performance of the proposed approach to flatten the building&apos;s load curve by optimally operating the energy storage system.

Název v anglickém jazyce

Energy management of buildings with energy storage and solar photovoltaic: A diversity in experience approach for deep reinforcement learning agents

Popis výsledku anglicky

Deep reinforcement learning (DRL) is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage systems. Conventionally, DRL agents are trained by randomly selecting samples from a data set, which can result in overexposure to some data categories and under/no exposure to other data categories. Thus, the trained model may be biased towards some data groups and underperform (provide suboptimal results) for data groups to which it was less exposed. To address this issue, diversity in experience-based DRL agent training framework is proposed in this study. This approach ensures the exposure of agents to all types of data. The proposed framework is implemented in two steps. In the first step, raw data are grouped into different clusters using the K-means clustering method. The clustered data is then arranged by stacking the data of one cluster on top of another. In the second step, a selection algorithm is proposed to select data from each cluster to train the DRL agent. The frequency of selection from each cluster is in proportion to the number of data points in that cluster and therefore named the proportional selection method. To analyze the performance of the proposed approach and compare the results with the conventional random selection method, two indices are proposed in this study: the flatness index and the divergence index. The model is trained using different data sets (1-year, 3-year, and 5-year) and also with the inclusion of solar photovoltaics. The simulation results confirmed the superior performance of the proposed approach to flatten the building&apos;s load curve by optimally operating the energy storage system.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

ENERGY AND AI

ISSN

2666-5468

e-ISSN

2666-5468

Svazek periodika

15

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

"Article Number: 100313"

Kód UT WoS článku

001108567700001

EID výsledku v databázi Scopus

2-s2.0-85175046551