The self-consumption without surplus to the grid is one of the aspects of the new Spanish law for prosumers. Increasing the share of renewable energy sources into the grid inherently leads to several constraints. The mismatch between the energy demand and the renewable energy production, which is intermittent in nature, is one of those challenges. Storage offers the possibility to decouple demand and supply, and therefore, it adds flexibility to the electric system. This research evaluates expanding electricity self-consumption without surplus to the grid by harnessing thermal mass storage in the residential sector. The methodology is investigated by using a variable refrigerant flow air conditioner system. Because there is no option to export the excess capacity to the grid, this research proposes an approach to profiting from this surplus energy by activating structural thermal mass, which is quantified from the information acquired using a building energy model. For this purpose, an EnergyPlus model of a flat in Pamplona (Spain) was used. The optimization analysis was based on a set-point modulation control strategy. Results show that under adequate climatological circumstances, the proposed methodology can reduce the total electric energy from the grid between by 60– $80\%$ .

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.