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.

翻译：医院因其复杂性和独特需求，在全球能源消费模式中扮演着关键角色。本研究采用PRISMA框架，对运用机器学习和人工智能技术预测医院建筑能耗的文献进行了全面综述。在识别的1884篇出版物中，有17篇涉及该特定领域，并经过深入评审以确立当前研究现状并识别未来研究所需的空白。综述揭示了影响能耗预测的多样化数据输入，其中占用率和气象数据成为重要预测因子。然而，许多研究未能深入探讨其数据选择的影响，且在对时间动态、运行状态和预处理方法的理解方面存在明显空白。机器学习，尤其是人工神经网络等深度学习模型，在该领域展现出潜力，但仍面临可解释性和计算需求等挑战。研究结果强调了人工智能在优化医院能耗方面的巨大潜力，同时也凸显了开展更全面、更细致研究的必要性。未来研究的关键领域包括人工神经网络方法的优化、新的优化与数据集成技术、将实时数据整合至智能能源管理系统，以及加强对长期能源预测的关注。