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. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.