Intelligent data systems for building energy workflow: Data pipelines, LSTM efficiency prediction and more

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21720/22:00359088

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Intelligent data systems for building energy workflow: Data pipelines, LSTM efficiency prediction and more

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

Intelligent data systems for building energy workflow: Data pipelines, LSTM efficiency prediction and more

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/TN01000056" target="_blank" >TN01000056: Centrum pokročilých materiálů a efektivních budov</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Energy and Buildings

ISSN

0378-7788

e-ISSN

1872-6178

Svazek periodika

267

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

000800348800001

EID výsledku v databázi Scopus

2-s2.0-85129786517