Intelligent data systems for building energy workflow: Data pipelines, LSTM efficiency prediction and more
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F22%3A00359088" target="_blank" >RIV/68407700:21220/22:00359088 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21720/22:00359088
Result on the web
<a href="https://doi.org/10.1016/j.enbuild.2022.112135" target="_blank" >https://doi.org/10.1016/j.enbuild.2022.112135</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.enbuild.2022.112135" target="_blank" >10.1016/j.enbuild.2022.112135</a>
Alternative languages
Result language
angličtina
Original language name
Intelligent data systems for building energy workflow: Data pipelines, LSTM efficiency prediction and more
Original language description
The data collection process for thermal energy storage (TES) system is largely still and restricted to data collection only. This leaves a gap to study the transient state physical process of charge and discharge as it proceeds. In addition, these devices are restricted and cannot perform on spot model fitting, prediction and other data curation techniques. This paper demonstrates the application of intelligent data layer with neural networks for evaluating and predicting end to end performance of heat pump integrated stratified thermal energy storage (TES) system. The data modelling – acquisition, curation, and transformation is done in situ (dynamically). The key objectives are: A method to demonstrate the application of data-layer framework to visualize in real-time energy efficiency of TES. To fit the second law of thermodynamics-based exergy model. This will help engineers to intuitively understand the energy efficiency of their devices using novel data pipeline. To demonstrate the use-case of hyper-tuned DL framework of LSTM to predict the energy efficiency in the process loop. Predicted results show tuned correlation with the parametrized experimental data, even during the load phase, where substantial amount of math (convection/mixing) is present for the network to learn (train/ test).
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20101 - Civil engineering
Result continuities
Project
<a href="/en/project/TN01000056" target="_blank" >TN01000056: Centre for Advanced Materials and Efficient Buildings</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy and Buildings
ISSN
0378-7788
e-ISSN
1872-6178
Volume of the periodical
267
Issue of the periodical within the volume
July
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
16
Pages from-to
—
UT code for WoS article
000800348800001
EID of the result in the Scopus database
2-s2.0-85129786517