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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

ENERGY AND AI

ISSN

2666-5468

e-ISSN

2666-5468

Volume of the periodical

15

Issue of the periodical within the volume

January

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

"Article Number: 100313"

UT code for WoS article

001108567700001

EID of the result in the Scopus database

2-s2.0-85175046551