Energy Efficiency
Real-time energy simulation and management with POWERBIM 6D
The POWERTWIN project represents the innovation engine and analytical brain behind our POWERBIM digital twin platform. It is a cyber-physical technological ecosystem specifically designed to address the inefficiency of Europe’s building stock — 70% of which is currently outdated and inefficient, directly accounting for nearly 40% of global CO₂ emissions.
The research is focused on real-time optimisation of sustainability and energy management indicators, aligned with the rigorous European Level(s) standards and nZEB (Nearly Zero-Energy Buildings) efficiency frameworks. Through the systematic integration of OpenBIM models, predictive thermodynamic simulations, and Artificial Intelligence algorithms, POWERTWIN enables the active modelling and control of the entire lifecycle of built assets.
The overall scope of this R&D&I initiative is structured around five key pillars:
- INTEGRATED THREE-DIMENSIONAL DIGITALISATION (BIM / GIS)
- HIGH-PERFORMANCE PREDICTIVE ENERGY SIMULATION (MATLAB)
- REAL-TIME MULTIVARIABLE IoT DATA INFRASTRUCTURE
- HIGH-CAPACITY DATA SCIENCE (AZURE DATALAKEHOUSE)
- AUTOMATED DEVIATION AND ISSUE ANALYSIS (openBIM BCF)
- INTEGRATED THREE-DIMENSIONAL DIGITALISATION (BIM / GIS)
- HIGH-PERFORMANCE PREDICTIVE ENERGY SIMULATION (MATLAB)
- REAL-TIME MULTIVARIABLE IoT DATA INFRASTRUCTURE
- HIGH-CAPACITY DATA SCIENCE (AZURE DATALAKEHOUSE)
- AUTOMATED DEVIATION AND ISSUE ANALYSIS (openBIM BCF)
Developed Technologies
Development of BIM and GIS digital models that strictly comply with the international ISO 19650 series standards and openBIM formats (IFC). The ecosystem enables open, silo-free collaboration through the exchange of issues in native BCF (BIM Collaboration Format) format.
Multivariable energy modelling through simulations in advanced environments such as MATLAB. This enables the calculation of the optimal or ideal thermal-energy performance of assets (such as the UPC Gaia pilot building) on an hourly basis over an entire annual period.
Deployment of a multivariable IoT sensor network across the building’s actual workspaces. The system continuously captures key dynamic data, including electricity consumption, occupancy levels across different areas, CO₂, and indoor thermal comfort parameters (humidity and temperature).
Implementation of a robust hybrid Data Lakehouse on the Microsoft Azure cloud. It enables the massive ingestion, storage, organisation, structuring, and cleansing of data from ideal simulations and actual energy consumption for advanced analytical processing.
Artificial Intelligence algorithms that perform Data Discovery techniques on the Data Lakehouse. The system automatically compares ideal simulation data with real-world IoT data, detecting deviations, inefficiency patterns, and triggering dynamic alerts and notifications.