This study proposes a comprehensive artificial intelligence (AI)-based framework to predict, disaggregate, and optimize energy consumption and associated CO₂ emissions across a multi-building university campus. Leveraging real-world data from 27 buildings at the University of Valladolid (Spain), six AI models—artificial neural networks (ANN), radial basis function (RBF), autoencoders, random forest (RF), XGBoost, and decision trees—were trained on heat exchanger performance metrics and contextual building parameters. The models were validated using an extensive set of key performance indicators (MAPE, RMSE, R², KGE, NSE) to ensure both predictive accuracy and generalizability. The ANN, RBF, and autoencoder models exhibited the highest correlation with actual data (R > 0.99) and lowest error rates, indicating strong suitability for operational deployment. A detailed analysis at building level revealed heterogeneity in energy demand patterns and model sensitivities, emphasizing the need for tailored forecasting approaches. Forecasts for a 5-year horizon further demonstrated that, without intervention, energy consumption and CO₂ emissions are projected to increase significantly, underscoring the relevance of predictive control strategies. This research establishes a robust and scalable methodology for campus-wide energy planning and offers a data-driven pathway for CO₂ mitigation aligned with European climate targets.

This study presents a comprehensive analysis of the energy efficiency and sustainability of Variable Refrigerant Flow (VRF) systems in university buildings during the winter season, offering significant contributions to the field. A novel methodology is introduced to accurately assess the real Seasonal Coefficient of Performance (SCOP) of VRF systems, benchmarked against conventional Heating, Ventilation, and Air Conditioning (HVAC) technologies, such as natural gas-fueled boiler systems. The findings demonstrate outstanding seasonal energy performance, with the VRF system achieving a SCOP of 5.349, resulting in substantial energy savings and enhanced sustainability. Key outcomes include a 67% reduction in primary energy consumption and a 79% decrease in greenhouse gas emissions per square meter when compared to traditional boiler systems. Furthermore, VRF systems meet 83% of the building’s energy demand through renewable energy sources, exceeding the regulatory SCOP threshold of 2.5. These results underscore the transformative potential of VRF systems in achieving nearly Zero-Energy Building (nZEB) objectives, illustrating their ability to exceed stringent sustainability standards. The research emphasizes the strategic importance of adopting advanced HVAC solutions, particularly in regions with high heating demands, such as those characterized by continental climates. VRF systems emerge as a superior alternative, optimizing energy consumption while significantly reducing the environmental footprint of buildings. By contributing to global sustainable development and climate change mitigation efforts, this study advocates for the widespread adoption of VRF systems, positioning them as a critical component in the transition toward a sustainable, zero-energy building future.

Hospital pathology laboratories represent challenging healthcare environments for Indoor Air Quality (IAQ) management due to the coexistence of hazardous chemical emissions, transient pollutant peaks, and stringent ventilation requirements. This study proposes an integrated data-driven framework for IAQ assessment and predictive ventilation management based on a high-resolution monitoring campaign conducted over 17 calendar days in a pathology grossing room (V = 113.90 m³) and an adjacent chemical storage room (V = 56.03 m³). The monitoring system generated 4356 synchronized 1-min observations, of which 4232 complete multivariate records were retained after data-quality screening. The proposed methodology combines three complementary analytical layers: (i) predictive modelling of pollution episodes using supervised machine learning architectures; (ii) multivariate anomaly detection to identify atypical environmental states; and (iii) temporal dependency analysis based on Granger causality and Bayesian networks to investigate predictive relationships between occupancy-related indicators, ventilation behaviour, and pollutant evolution. This integrated framework enables the transition from descriptive IAQ assessment toward predictive environmental management in healthcare facilities. Baseline statistical diagnostics demonstrated the limited capability of conventional linear approaches, with an Ordinary Least Squares (OLS) model explaining only 7.4% of TVOC variability (R² = 0.074). Ventilation assessment identified an approximately 38% deficit relative to the selected ASHRAE 170 ventilation requirement in the monitored grossing room. Among the evaluated predictive models, Random Forest achieved the highest test-set performance (R² = 0.78; MAE = 10.5 ppb), enabling short-term forecasting of TVOC evolution. Isolation Forest identified 212 atypical environmental states, corresponding to 5.01% of the valid analytical observations, with substantially higher TVOC concentrations than under normal operating conditions. The proposed framework establishes a transferable methodology for predictive IAQ assessment and ventilation management in chemically intensive healthcare facilities, providing decision-support information for risk-informed HVAC operation within existing regulatory and ventilation requirements.