Exploring artificial intelligence methods for energy prediction in healthcare Facilities: An In-Depth extended systematic review

Hospitals, due to their complexity and unique requirements, play a pivotal role in global energy consumption patterns. This study conducted a comprehensive literature review, utilizing thePRISMA framework,of articles that employed machine learning and artificial intelligence techniques for predicting energy consumption in hospital buildings. Of the2,157publications identified,35specifically addressed this domain and were thoroughly reviewed to establish the state-of-the-art and identify research gaps. The review revealed a diverse range of data inputs influencing energy prediction, with occupancy and meteorological data emerging as significant predictors. However, many studies did not delve deeply into the implications of their data choices, highlighting gaps in understanding time dynamics, operational status, and preprocessing methods.Machine learning, especially deep learning models like artificial neural networks (ANNs),showed potential in this domain but faced challenges, including interpretability and computational demands. Our study emphasized the necessity for detailed daily activity data and a broader spectrum of meteorological inputs to enhance prediction accuracy.Advanced data preprocessing and feature engineering techniqueswere identified as crucial for improving model performance. The integration of real-time data into Intelligent Energy Management Systems (IEMS) and long-term energy forecasting are areas that future research should prioritize for holistic sustainability in healthcare facilities. Additionally, the exploration of hybrid optimization strategies and enhancing model interpretability were recognized as pivotal for advancing the application of AI in this field. By addressing these areas, future research can significantly contribute to developing more efficient and sustainable energy management practices in hospitals. The findings underscore the immense potential of AI in optimizing hospital energy consumption but also highlight the need for more comprehensive and granular research.