Why under-modeling building performance leads to misallocated infrastructure and incentive investment
Utilities have never treated buildings as passive.
Load forecasting, electrification planning, peak demand management, and outage response all depend on assumptions about how buildings behave under changing conditions. Heat, cold, occupancy, and system constraints shape demand every day — and utilities model these dynamics continuously.
And yet, when it comes to how buildings are approved, incentivized, and planned around, they are still largely treated as static assets: compliant or not, efficient or inefficient, connected or disconnected.
That disconnect isn’t philosophical. It’s structural — and it shapes how billions of dollars in infrastructure and program capital are allocated.
People working in high-performance and above-code pathways already know this. Buildings don’t simply consume energy — they respond to stress. They buffer or amplify external conditions. They fail in patterned ways, or, when designed and built well, continue to perform when surrounding systems are strained.
A robust envelope behaves differently during extreme heat. A well-ventilated building manages indoor conditions differently during smoke events. A highly efficient building rides through outages differently than a code-minimum structure.
These differences are not marginal. They materially affect peak load, load shape, volatility, and system risk — the very variables utilities plan and invest around.
Yet once buildings move beyond design and construction, those performance expectations largely disappear from planning view. Buildings explicitly designed to behave differently are still treated the same once they are connected to the grid.
Utilities and municipalities are not short on goals. Electrification targets are accelerating. Resilience planning is becoming more urgent. Climate volatility is now a baseline planning assumption.
The challenge isn’t that the performance gap between high-performance and baseline buildings is unclear — it’s that modeled expectations are rarely reconciled with observed outcomes once buildings are in use, in a way that informs future planning decisions.
Upstream, utilities and program administrators rely on models and assumptions:
Downstream, they observe:
What’s missing is systematic feedback between the two — a way to test whether planning assumptions hold once buildings are operating at scale.
As a result, utilities can see program spend and participation counts — but not how specific performance assumptions translate into real-world behavior. Buildings that reduce peak demand, stabilize load, or perform better under stress are flattened into averages.
This has real consequences.
When high-performance buildings are under-modeled, utilities are left planning for higher peaks, greater volatility, and more infrastructure capacity than may actually be necessary under more accurate assumptions. Capacity investments are sized around conservative assumptions that fail to account for assets that behave differently once deployed.
At the same time, above-code incentives and programs are often evaluated primarily on participation and near-term spend — not on how much future infrastructure, risk, or capacity they may actually avoid.
The result is a double bind:
This is not a critique of utilities. It is a natural outcome of planning without feedback.
Above-code and high-performance buildings make this mismatch visible because they come with explicit performance expectations. Envelope quality, system design, and behavior under stress are not incidental — they are intentional.
But once construction is complete and the meter is spinning, those expectations are rarely revisited in a way that informs future planning.
There is no consistent way to ask:
Without those answers, utilities cannot fully value the assets they are already helping to create.
What if buildings were treated not as static endpoints, but as infrastructure assets whose performance could inform future planning and investment?
Not through new mandates or expanded reporting requirements, but by closing the feedback loop between:
This isn’t about generating more data. It’s about aligning capital decisions with real performance — and allowing utilities to learn, over time, which investments actually reduce long-term system costs and planning risk.
When that feedback exists, incentives stop being expenses. They become signals.
PassivSure emerged from examining the gap between how buildings are designed to perform and how their performance is represented once they are part of planning and investment systems.
What became clear is that the performance gap between high-performance buildings and baseline buildings is real, material, and economically consequential — and that it is systematically underrepresented in planning models.
When that gap is flattened, utilities don’t just miss insight. They systematically misallocate capital relative to the performance of the assets they are already enabling.
If you’re working at the intersection of buildings, utilities, or municipal systems — especially in above-code or high-performance pathways — I’d welcome the chance to compare notes.
