Energy Consumption Forecasting in Public Nursing Homes Using Multivariable Regression Models

Buildings represent 40% of the European Union’s energy consumption and 36% of its greenhouse gas emissions. Nursing homes are among the buildings that consume the most energy. The objective of this study was to make predictive models of Energy Consumption, Energy Costs, and CO2 Emissions in nursing homes using different variables. To do this, data from 20 public nursing homes located in Extremadura (Spain) during the 2019–2023 period were analyzed. All the buildings were built or renovated between 1995 and 2009; the useful area and the number of residents were in the range of 1332–10,880 m2 and 24–254 residents. A statistical analysis was performed using multivariable linear regression. During the research, equations that allow for the estimation of the annual Energy Consumption, Energy Costs and CO2 Emissions of nursing homes, according to the useful area and number of residents, were found. The Radj2 was 0.9710, 0.9744 and 0.9742, respectively. The quality of the models obtained was contrasted using the mean absolute error (MAE), the relative error (RE) and the root mean square error (RMSE), together with the assessment of multicollinearity through the Variance Inflation Factor (VIF). The findings of this study may prove beneficial for stakeholders within the elder care sector.