Integrating Artificial Intelligence with Building Information Modeling (BIM) for Predicting Energy Consumption in High-Density Urban Hospitals: Review

Due to their unique requirements and complexity, hospitals have a big influence on global energy usage trends. For this inquiry, a comprehensive review of the literature was conducted. The analysis concludes that while a wide range of data inputs influence energy prediction, weather and occupancy data are particularly significant predictors. However, a number of studies failed to fully analyze the implications of the data they chose, revealing gaps in our understanding of time dynamics, operational status, and preprocessing methods. Interpretability and processing demands were among the difficulties with machine learning, despite its potential, particularly with regard to deep learning models like artificial neural networks. Our analysis showed that in order to improve prediction accuracy, more comprehensive daily activity data and a wider variety of meteorological inputs are required. It was discovered that sophisticated feature engineering and data preparation methods were necessary to enhance model performance. Future studies should focus on long-term energy forecasting and integrating realtime data into Intelligent Energy Management Systems for total sustainability in healthcare institutions. It was also acknowledged that improving model interpretability and investigating hybrid optimization techniques were necessary to expand the use of AI in this area. Future studies can greatly aid in the development of more effective and sustainable hospital energy management procedures by tackling these issues. The results show how AI has a lot of potential to optimize hospital energy use, but they also show how much more thorough and in-depth research is required.