  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 the PRISMA framework, of articles
that employed machine learning and artificial intelligence techniques for
predicting energy consumption in hospital buildings. Of the 1884 publications
identified, 17 were found to address this specific domain and have been
thoroughly reviewed to establish the state-of-the-art and identify gaps where
future research is needed. This review revealed a diverse range of data inputs
influencing energy prediction, with occupancy and meteorological data emerging
as significant predictors. However, many studies failed to delve deep into the
implications of their data choices, and gaps were evident regarding the
understanding of time dynamics, operational status, and preprocessing methods.
Machine learning, especially deep learning models like ANNs, have shown
potential in this domain, yet they come with challenges, including
interpretability and computational demands. The findings underscore the immense
potential of AI in optimizing hospital energy consumption but also highlight
the need for more comprehensive and granular research. Key areas for future
research include the optimization of ANN approaches, new optimization and data
integration techniques, the integration of real-time data into Intelligent
Energy Management Systems, and increasing focus on long-term energy
forecasting.
