The urgency to retrofit buildings for energy efficiency highlights the need for effective financing mechanisms. Energy Performance Contracts (EPCs) present a viable solution by financing building retrofits based on anticipated energy savings. Reliable baseline models are essential to quantifying these savings accurately. EPCs facilitate retrofits by allowing Energy Service Companies (ESCOs) to cover the upfront costs of energy-saving measures, with repayment derived from the cost savings generated by the reduced energy consumption. This performance-based approach demands accurate and reliable baseline models to predict the expected savings. This study introduces a white-box calibration methodology that accurately estimates energy consumption even with limited monitoring data, making it valuable for cases with scarce or incomplete historical data. In addition to addressing data limitations, the research examines scenarios with restricted control parameters, demonstrating that indoor temperature and energy demand are essential to obtaining a robust baseline model. The present work focuses on performing the calibration process through a single-stage approach that operates on EnergyPlus’ Ideal Loads component and the building-envelope parameters simultaneously. The paper demonstrates that it is possible to accurately assess the building’s energy performance and capture its indoor climate while reducing the time and resources required to train the model. This method achieved a Coefficient of Variation of Mean Square Error (CV(RMSE)) of 26.40% and a Normalized Mean Bias Error (NMBE) of −8.49% during training, with stability maintained during the checking period. The resulting calibrated white-box model serves as a powerful tool for EPCs, enabling reliable prediction of energy savings and offering a predictive framework for building management. By incorporating both energy and temperature, the model supports more informed decision-making and proactive energy management, enhancing the overall sustainability and efficiency of building operations. The methodology is limited to air-based HVAC systems and depends on high-resolution data and monitoring infrastructure. Additionally, the methodology was tested on a single demonstration site, and further research is needed to assess its adaptability to diverse building types and HVAC configurations.

European directives advocate for end-users to be aware of their energy consumption. However, individual energy monitoring tools, such as energy meters or cost allocators, are not always affordable or technically feasible to install. Therefore, the development of virtual tools that enable the study of energy consumption in existing buildings is necessary. Virtual sensors, particularly based on white-box models, offer the opportunity to recreate these behaviours. When calibrated with measured data, white-box models, which incorporate detailed building physics, become increasingly valuable for designing energy-efficient buildings. This research explores a novel approach to identifying building’s load period directly from energy data generated by these calibrated models. The volume of data generated by white-box models can be overwhelming for visual analysis, but the hypothesis here is that analysing this data through clustering techniques can reveal patterns related to occupant behaviour and operational schedules. By feeding indoor temperature data into the calibrated model and analysing the resulting energy outputs, the research proposes a method to identify the heating, ventilation and air conditioning (HVAC) system operation schedule, free oscillation periods and non-recurrent events. Validation is achieved by comparing the identified periods with actual measured data. This methodology enables the development of a virtual sensor for cost allocation, which minimises the need for physical sensor deployment while complying with European Union directives. The research not only demonstrates high accuracy but also the potential to outperform measured schedule. This suggests the ability of the method to identify missing sensor data or other factors affecting temperature curves, enabling fault detection and diagnostics (FDD). Consequently, this opens doors for setting optimised operation schedules that balance energy efficiency with occupant comfort.