Optimization and Prediction of Office Building Shading Devices for Energy, Daylight, and View Consideration Using Genetic and Bo-Lgbm Algorithms DOI
Hangyue Zhang,

Yanqiu Cui,

Hongbin Cai

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

AI-Driven Design Optimization for Sustainable Buildings: A Systematic Review DOI Creative Commons

Piragash Manmatharasan,

Girma Bitsuamlak, Katarina Grolinger

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115440 - 115440

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

3
A multi-energy meta-model strategy for multi-step ahead energy load forecasting DOI Creative Commons
Aristeidis Mystakidis,

Evangelia Ntozi,

Paraskevas Koukaras

и другие.

Electrical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

2
Integrating Machine Learning and Genetic Algorithms to Optimize Building Energy and Thermal Efficiency Under Historical and Future Climate Scenarios DOI Open Access
Alireza Karimi, M. Mohajerani, Niloufar Alinasab

и другие.

Sustainability, Год журнала: 2024, Номер 16(21), С. 9324 - 9324

Опубликована: Окт. 27, 2024

As the global energy demand rises and climate change creates more challenges, optimizing performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency their time-consuming nature, limiting practical application. This study introduces a new framework that integrates Bayesian optimization, XGBoost algorithms, multi-objective genetic algorithms (GA) enhance building metrics—total (TE), indoor overheating degree (IOD), predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), future (2080) scenarios. The employs IOD as key indicator (KPI) optimize design operation. While traditional indices such mean vote (PMV) thermal sensation (TSV) are widely used, they fail capture individual comfort variations dynamic nature conditions. addresses these gaps by providing comprehensive objective measure discomfort, quantifying both frequency severity events. Alongside IOD, use intensity (EUI) index is used assess consumption per unit area, critical insights into efficiency. integration with EUI PPD enhances overall assessment performance, creating precise holistic framework. combination ensures efficiency, comfort, occupant well-being optimized in tandem. By addressing significant gap existing methodologies, current approach combines advanced techniques modern simulation tools EnergyPlus, resulting efficient accurate model performance. reduces time Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified factors influence metrics. Specifically, window-to-wall ratio (WWR) impacts TE increasing through higher heat gain cooling demand. Outdoor temperature (Tout) has complex effect on depending seasonal conditions, while (Tin) minor impact TE. For PPD, Tout major negative factor, indicating improved natural ventilation can reduce whereas Tin larger open areas exacerbate it. Regarding WWR significantly affect internal gains, windows temperatures contributing increased reduced comfort. also positive its varying over time. demonstrates conditions evolve, effects become pronounced, highlighting need for effective management envelopes HVAC systems.

Язык: Английский

Процитировано

8
Neural ordinary differential equations-based approach for enhanced building energy modeling on small datasets DOI
Zhihao Ma, Gang Yi Jiang, Jianli Chen

и другие.

Building Simulation, Год журнала: 2025, Номер unknown

Опубликована: Апрель 11, 2025

Язык: Английский

Процитировано

1
Sensitivity analysis of multiple time-scale building energy using Bayesian adaptive spline surfaces DOI
Hu Zhang, Wei Tian,

Jingyuan Tan

и другие.

Applied Energy, Год журнала: 2024, Номер 363, С. 123042 - 123042

Опубликована: Март 22, 2024

Язык: Английский

Процитировано

6
Optimization and prediction of office building shading devices for energy, daylight, and view consideration using genetic and BO-LGBM algorithms DOI
Hangyue Zhang,

Yanqiu Cui,

Hongbin Cai

и другие.

Energy and Buildings, Год журнала: 2024, Номер 324, С. 114939 - 114939

Опубликована: Окт. 30, 2024

Язык: Английский

Процитировано

6
Energy consumption dynamic prediction for HVAC systems based on feature clustering deconstruction and model training adaptation DOI
Huiheng Liu, Yanchen Liu, Huakun Huang

и другие.

Building Simulation, Год журнала: 2024, Номер 17(9), С. 1439 - 1460

Опубликована: Июль 19, 2024

Язык: Английский

Процитировано

5
GenFusion: Crafting Future Urban Building Layouts via Diffusion Model and Genetic Algorithm DOI Creative Commons

Jinding Gao,

C. Liang, Xiaocong Xu

и другие.

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Applications of Explainable Artificial Intelligence (XAI) and interpretable Artificial Intelligence (AI) in smart buildings and energy savings in buildings: A systematic review DOI
M. Haghighat,

Ehsan MohammadiSavadkoohi,

Niusha Shafiabady

и другие.

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112542 - 112542

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Machine learning-enhanced multi-objective optimization of integrated shading systems: Enhancing daylight availability, glare protection, and energy savings DOI

Wei Zhang,

Zhichao Ma, Han Qiu

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 113124 - 113124

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0