A Quantitative Approach to Modeling the Overconsumption of Energy in Healthcare Buildings

Healthcare buildings consume 2.6 times more energy than commercial buildings, primarily due to the operation of electromedical equipment and the stringent indoor environmental requirements for medical practice. This fact underscores the need for research aimed at addressing the high intensity of energy consumption. For this purpose, methods employing building energy simulation, mathematical correlation models, and machine learning algorithms have been developed to quantify the impact of various variables on energy consumption. However, the specific influence of individual attribute variations within these variables has not yet been fully disaggregated. To bridge this gap in knowledge, 70 healthcare buildings in Spain were analyzed to model the interaction among parameters in healthcare buildings affecting the overconsumption of energy. The quantitative multifactor dimensionality reduction method was employed to analyze the phenomenon under study by developing three models: final energy consumption, energy demand, and thermal energy consumption. Having more than 5000 assigned patients increases the likelihood of exceeding final energy consumption by 17.3%, relying on fossil fuel boilers for heating raises the risk of higher‐than‐average energy demand by 30.0%, and installing heat pumps with less than 5 kW power increases the risk of elevated thermal energy consumption by 15.0%–38.1%. Results indicate that energy consumption intensity is significantly influenced by demographic variables and the heating, ventilation, and air‐conditioning systems of healthcare buildings. The research findings will guide the design of new healthcare buildings and assist facility managers in more effectively allocating operational expenses.