Optimize building energy efficiency design and evaluation with machine learning

• Autorzy: Gu Chun

Identyfikatory

DOI

10.24425/ace.2025.153353

• Języki publikacji: EN

Abstrakty

EN

With the increasing demand for energy efficiency optimization in the building industry, this study explores the application of machine learning technology in building energy efficiency design and evaluation. By comprehensively analyzing energy consumption data, environmental factors, building characteristics, and user behavior patterns, this paper proposes a machine learning-based approach aimed at accurately predicting and improving the energy efficiency of buildings. The study collected and pre-processed a large amount of data, built and trained multiple models, including neural networks, which showed a high degree of predictive accuracy in cross-validation. The results show that the neural network has obvious advantages in the task of building energy efficiency prediction. In addition, the interpretability of the model in practical applications and future research directions, such as the introduction of real-time monitoring data and in-depth study of the interpretability of the model, are also discussed. This study not only provides a new perspective for building energy efficiency optimization, but also provides a practical tool for intelligent building design and operation.

Słowa kluczowe

EN

building; energy consumption; forecast; energy efficiency; design; evaluation; machine learning

PL

budynek; prognoza zużycia; zużycie energii; efektywność energetyczna; projektowanie; ocena; uczenie maszynowe

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2025
• Tom: Vol. 71, nr 1
• Strony: 615--629
• Opis fizyczny: Bibliogr. 21 poz., il., tab.

Twórcy

autor

Gu Chun

• Architectural Engineering Institute, Zhoukou Vocational and Technical College, Zhoukou, China

• https://orcid.org/0009-0004-8795-4794

Bibliografia

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