Low-energy residential building optimisation for energy and comfort enhancement in semi-arid climate conditions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24620%2F23%3A00011198" target="_blank" >RIV/46747885:24620/23:00011198 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0196890423006106" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0196890423006106</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.enconman.2023.117264" target="_blank" >10.1016/j.enconman.2023.117264</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Low-energy residential building optimisation for energy and comfort enhancement in semi-arid climate conditions

Popis výsledku v původním jazyce

The application of energy-efficient strategies in buildings, such as the Green Building Concept, can significantly impact human comfort and resource consumption. However, due to the complexity of decision-making factors and the variety of available materials, computational models are necessary to identify the most effective solutions and optimise building energy performance. This study presents an integrated framework that uses machine learning algorithms and a Petri Net control system to optimise the thermal, comfort, and energy efficiency of both vertical and horizontal building envelopes in semi-arid climate zones. The framework incorporates several passive techniques for building energy parameters, including material thickness and melting point, window types, wall insulation thickness and thermal emissivity, wall solar absorbance, window wall ratio, fenestration position, air tightness, roof solar reflectance, roof insulation thickness and conductivity (W/(m·°C)), and floor insulation thickness. An experiment design was developed using Box-Behnken Design-Response Surface Methodology (BBD-RSM) for statistical optimisation, which was coupled with Design Builder simulation model. The methodology was demonstrated by applying it to a residential building in Mexico. Meta Additive Regression was used to analyse the output factors, which showed higher confidence compared to REP Tree and M5P Tree algorithms in green buildings. The results demonstrate that an annual energy reduction of 50 kW/m2 per household can be achieved by using an optimised building envelope.

Název v anglickém jazyce

Low-energy residential building optimisation for energy and comfort enhancement in semi-arid climate conditions

Popis výsledku anglicky

The application of energy-efficient strategies in buildings, such as the Green Building Concept, can significantly impact human comfort and resource consumption. However, due to the complexity of decision-making factors and the variety of available materials, computational models are necessary to identify the most effective solutions and optimise building energy performance. This study presents an integrated framework that uses machine learning algorithms and a Petri Net control system to optimise the thermal, comfort, and energy efficiency of both vertical and horizontal building envelopes in semi-arid climate zones. The framework incorporates several passive techniques for building energy parameters, including material thickness and melting point, window types, wall insulation thickness and thermal emissivity, wall solar absorbance, window wall ratio, fenestration position, air tightness, roof solar reflectance, roof insulation thickness and conductivity (W/(m·°C)), and floor insulation thickness. An experiment design was developed using Box-Behnken Design-Response Surface Methodology (BBD-RSM) for statistical optimisation, which was coupled with Design Builder simulation model. The methodology was demonstrated by applying it to a residential building in Mexico. Meta Additive Regression was used to analyse the output factors, which showed higher confidence compared to REP Tree and M5P Tree algorithms in green buildings. The results demonstrate that an annual energy reduction of 50 kW/m2 per household can be achieved by using an optimised building envelope.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20302 - Applied mechanics

Projekt

<a href="/cs/project/LM2023066" target="_blank" >LM2023066: Nanomateriály a nanotechnologie pro ochranu životního prostředí a udržitelnou budoucnost</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Energy Conversion and Management

ISSN

0196-8904

e-ISSN

—

Svazek periodika

291

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

001029015700001

EID výsledku v databázi Scopus

2-s2.0-85162150229