Enterprise AI Analysis
Digital Twin-Based Simulation of Smart Building Energy Performance: BIM-Integrated MATLAB/Simulink Framework for BACS and SRI Evaluation
This paper introduces a digital twin-based simulation framework that integrates BIM data with MATLAB/Simulink for evaluating building automation and control strategies. It enables scenario-based analysis of automation maturity levels (conventional, advanced, predictive) aligned with EN ISO 52120 and the Smart Readiness Indicator (SRI). The framework supports reproducible modeling of HVAC, lighting, and shading controls to compare energy-related behavior under unified conditions. Results show increased automation sophistication captures performance trends and reveals subsystem interactions. The methodology provides a practical foundation for early-stage, regulation-aligned assessment of smart building solutions.
Key Impact Metrics
Our analysis reveals the transformative potential of advanced building automation:
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Total Daily Energy Savings (Baseline to Predictive)
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Weekly Energy Savings (Advanced BACS)
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SRI Alignment Score (High-SRI Config)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Digital Twin Framework Overview
The proposed framework integrates Building Information Modeling (BIM)-derived spatial and semantic data with MATLAB/Simulink models to create a dynamic digital twin. This enables regulation-oriented evaluation of building automation and control strategies. It facilitates scenario-based analysis of automation maturity levels, covering conventional, advanced, and predictive configurations aligned with EN ISO 52120 and the Smart Readiness Indicator (SRI). The framework provides a reproducible environment for modeling various building systems and their interactions.
HVAC System Modeling in Simulink
The HVAC subsystem is implemented using Simscape libraries within MATLAB/Simulink, integrating thermal zoning, air-water heat pump operation, hydraulic flow control, and digital automation logic. This unified framework allows for concurrent analysis of both physical behavior and control algorithms, crucial for evaluating BACS functionalities under EN ISO 52120 and SRI Method C. The heat pump is modeled as a closed-loop temperature controller with mode selection and hydraulic flow management, using a discretized error and PID control for valve actuation.
Lighting System Modeling and Control
The lighting subsystem is incorporated into the MATLAB/Simulink and Simscape framework to analyze its impact on energy performance and smart-control functionality. The model supports various configurations representing different complexity levels, aligning with EN ISO 52120 and SRI. It uses rule-based control combining time-based occupancy patterns, orientation-dependent solar modeling, and configurable dimming based on simulated daylight availability. Occupancy-based control includes randomized operation patterns for shared spaces to enhance energy efficiency and user experience.
Scenario-Based Automation Analysis
The study evaluates three representative scenarios: a baseline BACS Class C, an advanced Class A, and a high-SRI predictive setup. Scenario A employs simple, independent controls (fixed setpoints, on/off lighting). Scenario B introduces coordinated, adaptive strategies (occupancy-proportional ventilation, daylight-responsive dimming, heat recovery). Scenario C integrates predictive control, real-time optimization, and weather-based forecasting through model predictive control (MPC), representing the highest level of smart readiness.
Comparative Performance Evaluation
The comparative analysis across scenarios A, B, and C reveals a substantial reduction in total energy consumption when transitioning from baseline Class C to advanced Class A, and further, albeit smaller, gains with the high-SRI predictive configuration. Scenario B achieves significant savings through demand-controlled ventilation and daylight dimming. Scenario C, with its predictive and integrated controls, demonstrates the lowest overall energy consumption, highlighting the benefits of proactive management, improved thermal stability, and grid responsiveness, despite a slight increase in HVAC energy due to tighter environmental control and IAQ requirements.
51.8%
Reduction in Total Daily Energy Consumption (Baseline to Predictive)
Enterprise Process Flow: Digital Twin Simulation Workflow
BIM Data Integration
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MATLAB/Simulink Modeling
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BACS/SRI Logic Implementation
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Scenario-Based Simulation
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Performance Evaluation
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Regulation Alignment
| Feature | Scenario A (Class C) | Scenario B (Class A) | Scenario C (High SRI) |
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| Heating Control |
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| Lighting Control |
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| Ventilation & AC |
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| Solar Gain (Blinds) |
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| Energy Reporting |
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Case Study: Predictive Control's Transformative Impact
Scenario C demonstrated a 51.8% reduction in total daily energy consumption compared to the baseline, achieving the lowest energy use across all configurations. This was driven by predictive setpoint optimization, IAQ-driven demand control ventilation, and adaptive lighting scenes, all integrated within a high-SRI framework. The predictive blind control further reduced cooling demand by proactively managing solar gains, leading to superior thermal stability and operational robustness. This highlights the profound efficiency gains achievable with advanced, AI-assisted automation.
91.7%
Smart Readiness Indicator (SRI) Alignment Achieved in Predictive Scenario
Calculate Your Potential ROI with Digital Twins
Estimate the energy savings and operational efficiency gains your organization could achieve with a BIM-integrated Digital Twin solution.
Estimated Annual Savings
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Equivalent Hours Reclaimed
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Your Digital Twin Implementation Roadmap
A phased approach to integrate BIM-driven Digital Twins and advanced BACS into your operations.
Phase 1: Foundation & BIM Integration (0-3 months)
Refine BIM-derived data structures and conversion workflows. Establish robust thermal models in Simulink, focusing on computational efficiency and reproducibility for building geometry and thermal properties.
Phase 2: BACS Logic & Scenario Development (3-9 months)
Implement advanced BACS logic for HVAC, lighting, and shading control functions. Develop diverse automation scenarios aligned with EN ISO 52120 and SRI guidelines, enabling detailed performance comparison across maturity levels.
Phase 3: Predictive Control & AI Enhancement (9-15 months)
Integrate predictive control algorithms (Model Predictive Control) and AI-assisted strategies for real-time optimization. Develop advanced Indoor Air Quality (IAQ) modeling and dynamic grid-responsive features for demand-side management.
Phase 4: Validation & Regulation Alignment (15-24 months)
Conduct extensive model calibration and validation against operational data. Implement full SRI Method C quantification and develop tools for regulation-aligned smart-building design and comprehensive assessment.
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