The team presents the project
The research explores the potential of building automation solutions in the transformation of existing buildings into smart buildings, utilizing the systems already present in the building itself. In particular, the results achievable through advanced control solutions based on Model Predictive Control (MPC) will be evaluated. After identifying a case-study building, a simplified model will be developed, calibrated, and validated. This model, with the acquisition and simulation of contextual information (e.g., occupancy profiles and predicted weather data), can be used to dynamically optimize the building-system control. The objective is to maximize comfort for occupants and minimize the building’s energy consumption, while increasing the use of renewable energy sources. The developed control model will be based on a cloud interface, allowing remote user access and raising awareness about the most efficient solutions.
This project promotes sustainability
European buildings account for over 40 % of final energy consumption and generate more than one-third of climate-altering emissions. Reducing their energy impact is now seen as a necessity at the European, national, and local levels. Following the introduction of energy certification systems, nearly-zero-energy buildings, and energy retrofit plans for existing building stock, the latest EU directive has assigned a key role to building automation technologies in the energy retrofitting of buildings. The development of intelligent plant controls and regulations can indeed lead to significant energy savings, making it a less invasive intervention compared to alternative measures and therefore more easily implementable across regions.
Structure, methods, and project activities
The research project has a duration of 12 months (from 15.03.2023 to 14.03.2024) and is structured into the following tasks:
- Task1, “State-of-the-art overview” (months M1-M3), which involves the analysis of the state of the art and literature with the aim of identifying best practices and KPIs for evaluating the effectiveness of MPC solutions.
- Task2, “Identification of a case study building,” and Task3, “Evaluation of the case study and installation of sensors” (months M3-M5), in which the identification of the case-study building is carried out, and a network of sensors and actuators is installed for its monitoring and control.
- Task4, “Development and calibration of digital building models for MPC” (months M6-M7), in which various digital energy models of the case-study building are developed, each with a different level of detail.
- Task5, “Monitoring and testing” (months M6-M12), involves monitoring the energy performance and indoor conditions of the building and implementing new control logics using the Model Predictive Control approach, utilizing the different digital energy models developed to identify the optimal configuration for validation through a concluding testing activity.
- Task6, “Analysis and dissemination of results” (months M6-M12), which includes data analysis and their comparative simulation with traditional systems, as well as the dissemination of the results.