Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Integrating machine learning in architectural engineering sustainable design: a sub-hourly approach to energy and indoor climate management in buildings
This study predicts building energy use and indoor climate using CNNs and LOA. The research gives architects and engineers real-time building performance optimization tools using sub-hourly data to meet escalating energy efficiency and interior environment needs. This research presents a new way for improving prediction model accuracy and efficiency using CNN's spatial and temporal data processing and LOA's systematic feature selection. LOA and CNN identified the most critical building energy consumption and indoor climate variables, creating a predictive model with enhanced R-squared and Mean Absolute Error. According to the main findings, machine learning can recognize and quantify complex energy use and interior environment characteristics. The LOA-CNN combination provided a predictive model with better accuracy and efficiency, highlighting environmental and operational factors, and offering a granular picture of sustainable building management. This research reveals applications that could change architectural engineering beyond theoretical. This study uses predictive analytics to precisely regulate and optimize building systems, enabling the development of more intelligent, more sustainable buildings that dynamically respond to occupant needs and environmental circumstances, boosting sustainability and well-being. This study's CNN-LOA integration indicates advanced machine learning techniques may be used in sustainable building design, ushering in data-driven energy efficiency and indoor environmental quality innovation.
Integrating machine learning in architectural engineering sustainable design: a sub-hourly approach to energy and indoor climate management in buildings
This study predicts building energy use and indoor climate using CNNs and LOA. The research gives architects and engineers real-time building performance optimization tools using sub-hourly data to meet escalating energy efficiency and interior environment needs. This research presents a new way for improving prediction model accuracy and efficiency using CNN's spatial and temporal data processing and LOA's systematic feature selection. LOA and CNN identified the most critical building energy consumption and indoor climate variables, creating a predictive model with enhanced R-squared and Mean Absolute Error. According to the main findings, machine learning can recognize and quantify complex energy use and interior environment characteristics. The LOA-CNN combination provided a predictive model with better accuracy and efficiency, highlighting environmental and operational factors, and offering a granular picture of sustainable building management. This research reveals applications that could change architectural engineering beyond theoretical. This study uses predictive analytics to precisely regulate and optimize building systems, enabling the development of more intelligent, more sustainable buildings that dynamically respond to occupant needs and environmental circumstances, boosting sustainability and well-being. This study's CNN-LOA integration indicates advanced machine learning techniques may be used in sustainable building design, ushering in data-driven energy efficiency and indoor environmental quality innovation.
Integrating machine learning in architectural engineering sustainable design: a sub-hourly approach to energy and indoor climate management in buildings
Asian J Civ Eng
Hussein, Mohammed Yousef Abu (Autor:in) / Musa, Akram (Autor:in) / Altaharwah, Yousef (Autor:in) / Al-Kfouf, Safa’ (Autor:in)
Asian Journal of Civil Engineering ; 25 ; 4107-4119
01.07.2024
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Machine learning , Sustainable architectural design , Energy efficiency , Convolutional neural networks (CNN) , Lion optimization algorithm (LOA) , Predictive modeling , Building performance optimization , Indoor climate management Engineering , Civil Engineering , Building Materials , Sustainable Architecture/Green Buildings
Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate
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