What happens to a building once design and construction are complete and the owner moves in? Maintenance happens - we hope. The reality is that spending thousands on BIM models, BAS systems, and ultra efficient designs yields poor results if the building is ill maintained or improperly operated.

The persistent gap between modeled building performance and actual operational outcomes represents one of the most significant challenges facing our industry. While design teams create sophisticated energy models predicting optimal performance, facility operators often have insufficient time, training, and tools needed to achieve these targets. This disconnect is exacerbated by chronically understaffed operations and maintenance crews who end up struggling with complex building systems and ineffective documentation.

This session explores innovative collaborative tools developed through integrated partnerships between designers and building operators to close this performance gap. We'll examine practical solutions that respect operational realities while preserving design intent, including automated scheduling systems that intelligently organize maintenance tasks by location, sequence prerequisites, and optimize intervals to prevent equipment degradation. These systems transform scattered maintenance requirements into coordinated workflows that use facility staff time efficiently and respectfully.

The session also introduces visual reference systems, with the option of digital BIM integration, rather than dense manuals that go unread. These tools capture and preserve institutional knowledge while providing immediate, practical guidance for operators managing complex building systems.

Additionally, we'll explore methods for leveraging existing building automation system data flows alongside original design-phase energy models to enhance measurement and verification processes. This approach creates feedback loops that inform operators whether buildings are achieving their full performance potential and provide actionable insights for optimization.

Through case studies and example documents, participants will learn how these collaborative tools not only improve building performance but also strengthen the crucial relationship between design and operations teams, ultimately delivering on the promise of high-performance buildings.

Uniqueness: Integrating BEMs with Real-Time Data Creates Game-Changing Use Cases

Hypothetical Question: What if I told you that every Building Energy Model you've ever created is an essential tool to be used in the operation of a building? How would you tackle that? What would you do first? This isn’t a hypothetical question anymore. It can be done today and starts by simply waking up your sleeping design models."

Problem: The Performance Gap Crisis is Real

Buildings consistently underperform design expectations by 30-80%. We invest hundreds of thousands in sophisticated BEMs during design. The moment construction ends, these valuable building energy models are retired. They are filed away in project folders, becoming "Sleeping Digital Twins. Meanwhile, clients are asking two questions we can't answer:

1) "Did I get what I paid for?"

2) "What is my building's optimum energy efficiency potential?"

Missing Link: The gap between design intentions and the operational performance of buildings will never be closed without a systematic verification of performance versus design expectations. During design, building energy models are created to predict how a building should be expected to perform in operations based on assumptions. However, in operations today, statistical regression analysis is the standard to use historical data to predict future performance. Physics-based models are not represented in the operational data technologies of buildings. The building energy models created, during design, to defend owners’ investments in the performance of new or existing buildings, cannot be leveraged in the ongoing operations of buildings.

Solution: Operational Building Energy Models

AUROS Insights™ bridges this gap -- the first platform integrating physics-based simulation with real-time operational data. What does this capability enable for building energy modelers?

New-to-the-World Use Cases: [with images of 3-5 use cases]

• Monitoring-based commissioning

• Whole-building efficiency and decarbonization planning

• Interrogation-based commissioning

• Test "what-if" scenarios virtually before spending capital

• Deploy advanced analytics with the context of how buildings should perform

• Technology-based functionality testing commissioning

• Automated alerts when buildings drift from design intent

• Demand response testing

• Prioritize investments across portfolios based on physics-based ROI

Quick review of GSA building case study:

Energy Savings: $154 million over 25 years

BPS Fine Avoidance: $23.5 million

Carbon Reduction: 137,000 metric tons CO2e

Performance Improvement: 86.2%

This transforms buildings from cost centers to performance assets.

What does the mean to you?

For Building Energy Modeling Professionals, this means: You go from design consultants to ongoing performance partners able to demonstrate performance gaps on day one of operations. Your models become the missing link between design intent and operational reality.

New Revenue Streams:

• Ongoing model calibration and maintenance contracts

• Performance verification services

• Optimization consulting based on your physics-based insights

Advances in HVAC system modeling and co‑simulation are enabling faster and more reliable development of building control strategies. This work presents a hardware‑in‑the‑loop (HIL) platform that integrates a high‑fidelity Functional Mock‑up Unit (FMU) exported from Dymola with an embedded controller executing production level HVAC algorithms. Leveraging the Functional Mock‑up Interface (FMI) standard, the platform ensures interoperability and seamless coupling between virtual plant models and physical control hardware.

The FMU models include heat pumps and chiller systems typical of mission‑critical and healthcare environments, where reliability, rapid response, and precise temperature control are essential. By executing the FMU in real time, the platform enables bi‑directional exchange of sensor and actuator signals, supporting accurate evaluation of control sequences, supervisory logic, and fault‑handling routines. The approach allows engineers to test a wide range of operational scenarios, load dynamics, and failure modes that would be difficult or risky to replicate on actual equipment.

Digital twins are often presented as powerful analytical or visualization tools, but their real value emerges when they are implemented as operational systems that actively support day-to-day building management. This presentation documents the real-world implementation of a digital twin across three office buildings, focusing on how design models, sensor networks, and cloud-based platforms were connected to enable live monitoring, insight generation, and operational control.

The project deployed a coordinated sensing and data strategy across all three buildings, integrating temperature, relative humidity, CO₂, and daylight sensors from multiple manufacturers. Rather than relying on a single proprietary ecosystem, the implementation prioritized interoperability, allowing equipment from different vendors to feed a unified digital twin environment. Sensor data is streamed continuously into the cloud and mapped to spatial building elements, systems, and zones within a 3D digital replica.

Using a structured digital twin workflow, defining operational goals, contributing system and sensor data, verifying accuracy, transitioning ownership to operations, and continuously monitoring performance, the digital twin evolved from a design-aligned model into a live operational asset. Facility teams can visualize near real-time conditions in context, quickly identify deviations from comfort or indoor air quality targets, and understand how individual assets contribute to whole-building performance.

The implementation goes beyond monitoring. Daylight sensor data informs lighting strategies, CO₂ measurements support demand-responsive ventilation, and temperature and humidity feedback enable refinement of HVAC operation based on actual building behavior rather than assumed schedules. By linking sensor data directly to operational decision-making, the digital twin creates a closed feedback loop between building performance and control actions.

This presentation shares practical lessons learned from deploying the digital twin at building scale, including challenges related to sensor placement, data validation, asset mapping, and cross-team coordination. It emphasizes that successful digital twin implementation depends as much on workflow design and data governance as it does on technology selection. The approach demonstrated across three office buildings provides a replicable roadmap for teams seeking to move from static models to truly operational digital twins that bridge simulation intent and real-world performance.

Historic building retrofits pursuing net-zero energy goals face unique challenges related to envelope constraints, heating-dominated climates, and HVAC system selection. This study summarizes a preliminary whole-building energy modeling study for Summit Mill, a 36,968 ft² historic building in Philmont, New York, targeting an all-electric, net-zero design. Using IESVE, three HVAC system configurations—air-to-water heat pumps, ground-source heat pumps, and air-cooled VRF with heat recovery—were evaluated under ASHRAE 90.1-2016 Energy Cost Budget and NYS Stretch Code requirements. Results demonstrate that the heating-dominated climate significantly impacts system performance, with ground-source heat pumps maintaining superior efficiency and capacity at low temperatures compared to air-source alternatives. The study highlights how system choice, climate sensitivity, and code compliance pathways influence feasibility for historic net-zero projects, offering practical insights for early-stage design decision-making.

The D.M. Smith Building at Georgia Institute of Technology, a 1920s-era academic building in the campus historic district is undergoing a transformative renovation targeting LEED Platinum certification. This project emphasizes an integrated design process in which whole-building energy simulation was deployed early and iteratively to guide key decisions. From the outset, the renovation served as a real-time case study aligning with Georgia Tech’s aggressive sustainability goals, including carbon neutrality and significant energy/water use reductions. Project stakeholders collaborated to ensure design choices support institute priorities in carbon reduction and historic preservation. Notably, the design incorporates campus climate action objectives by electrifying building systems (eliminating steam reliance) to cut fossil fuel use, all while respecting the structure’s heritage facade and character.

In terms of methodology, the project illustrates the academic rigor of simulation-driven retrofit planning. The design team developed detailed energy models and tested multiple scenarios to establish a realistic yet ambitious energy use intensity target that balances efficiency with budget and preservation constraints. Iterative analysis and sensitivity testing of various measures—such as high-performance windows, enhanced insulation, and innovative HVAC options—provided quantitative insight into their impact on performance. This early modeling workflow, coupled with continuous stakeholder input, guided the selection of strategies that maximize energy savings and occupant comfort without compromising the building’s historic fabric. The collaborative process also engaged faculty and students, reflecting feedback on sustainability priorities, occupant experience, and accessibility needs. The renovation is intended to improve indoor environmental quality and occupant comfort while supporting overall performance goals, directly responding to feedback gathered from building users during the programming process.

Beyond this single building, the D.M. Smith renovation is positioned as a replicable model and decision-making framework for future campus retrofits. It demonstrates how integrating simulation and interdisciplinary teamwork from project inception can inform institutional decisions on energy upgrades in existing buildings. The outcome is not an isolated retrofit, but a template for scaling up retrofitting efforts across campus, bridging Georgia Tech’s sustainability vision with practical implementation. The renovated facility will also function as a “living” learning tool for the campus community: its performance data, operational strategies, and post-occupancy feedback will be leveraged in academics and campus planning. This high-impact project showcases how a historic building can meet aggressive performance targets while balancing cost, comfort, and preservation, offering valuable insights for both researchers and practitioners in building performance simulation. The abstract highlights a professional, simulation-informed approach that merges high-level sustainability objectives with grounded project outcomes, exemplifying the potential of integrated design in achieving Georgia Tech’s long-term eco-goals.