Predicting Energy Consumption in Hospitals Using Machine Learning: A Data-Driven Approach to Energy Efficiency in the USA

In the USA, hospitals are confronted with significant challenges regarding energy consumption, which not only impacts operational costs but also contributes to environmental concerns. The primary objective of this research was to develop and evaluate machine learning models that are capable of accurately predicting energy consumption in U.S. hospitals. This study will be focused on United States hospital energy consumption data, recognizing the unique difficulties and opportunities present in the U.S. healthcare setting. The data used for this hospital energy consumption analysis has been carefully gathered from multiple credible sources, including the U.S. Department of Energy's Energy Star program, whole-building hospital energy audits, and information from local utility providers. This variety in sourcing guarantees a strong and complete dataset that accurately represents real-world energy dynamics in healthcare buildings. In the model selection phase, three powerful algorithms were employed: the Random Forest Classifier, XG-Boost, and Artificial Neural Network (ANN). XG-Boost outperformed other models after tuning, achieving an 81.8% accuracy on the test set. Random Forest showed a decent improvement post-tuning but still lagged behind XG-Boost. Hospital managers can utilize machine learning (ML)--based predictions to achieve substantial cost savings in operational expenditures related to energy usage. With predictive analytics, hospitals can anticipate energy needs based on several parameters, such as patient occupancy rates, time of day, and seasonality. Integration of AI-driven energy prediction in hospital sustainability plans has significant policy implications for the U.S. healthcare sector. The integration of machine learning models and the Internet of Things (IoT)-)-)-enabled energy management systems is a breakthrough step in embracing smart hospital initiatives.