Optimization of heating and cooling energy demand and indoor thermal comfort under oceanic climate conditions using machine learning and NSGA-II Algorithm: A case study of Prague, Czech Republic

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00387913" target="_blank" >RIV/68407700:21110/25:00387913 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21720/25:00387913

Výsledek na webu

<a href="https://doi.org/10.26868/25222708.2025.1539" target="_blank" >https://doi.org/10.26868/25222708.2025.1539</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.26868/25222708.2025.1539" target="_blank" >10.26868/25222708.2025.1539</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of heating and cooling energy demand and indoor thermal comfort under oceanic climate conditions using machine learning and NSGA-II Algorithm: A case study of Prague, Czech Republic

Popis výsledku v původním jazyce

Optimizing building energy performance and indoor thermal comfort is essential for enhancing sustainability and climate resilience. This study develops a multi-objective optimization (MOO) framework integrating machine learning algorithms with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize a typical residential building in Prague, Czech Republic. The optimization, conducted using jEPlus+EA software, minimizes annual heating and cooling energy loads while improving occupant thermal comfort, measured by the Predicted Percentage Dissatisfied (PPD) index. Sensitivity Analysis (SA) using SHapley Additive Explanations (SHAP) identifies the most influential 18 passive and 2 active retrofit strategies. The results indicate that optimal retrofit configurations can reduce heating and cooling energy demand by up to 89%, and 82% respectively while maintaining indoor thermal comfort.By integrating machine learning-driven optimization with SHAP-based sensitivity analysis, this study provides a computationally efficient, data-driven methodology for sustainable building retrofits. The proposed framework enables evidence-based decision-making, offering practical insights for balancing energy efficiency, thermal comfort, and climate adaptation in the Czech building sector.

Název v anglickém jazyce

Optimization of heating and cooling energy demand and indoor thermal comfort under oceanic climate conditions using machine learning and NSGA-II Algorithm: A case study of Prague, Czech Republic

Popis výsledku anglicky

Optimizing building energy performance and indoor thermal comfort is essential for enhancing sustainability and climate resilience. This study develops a multi-objective optimization (MOO) framework integrating machine learning algorithms with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize a typical residential building in Prague, Czech Republic. The optimization, conducted using jEPlus+EA software, minimizes annual heating and cooling energy loads while improving occupant thermal comfort, measured by the Predicted Percentage Dissatisfied (PPD) index. Sensitivity Analysis (SA) using SHapley Additive Explanations (SHAP) identifies the most influential 18 passive and 2 active retrofit strategies. The results indicate that optimal retrofit configurations can reduce heating and cooling energy demand by up to 89%, and 82% respectively while maintaining indoor thermal comfort.By integrating machine learning-driven optimization with SHAP-based sensitivity analysis, this study provides a computationally efficient, data-driven methodology for sustainable building retrofits. The proposed framework enables evidence-based decision-making, offering practical insights for balancing energy efficiency, thermal comfort, and climate adaptation in the Czech building sector.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

—

Návaznosti

R - Projekt Ramcoveho programu EK

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of Building Simulation 2025: 19th Conference of IBPSA

ISBN

978-1-7750520-4-3

ISSN

2522-2708

e-ISSN

2522-2708

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

International Building Performance Simulation Association

Místo vydání

—

Místo konání akce

Brisbane

Datum konání akce

24. 8. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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