Why Your District Heating Network Is Probably Oversized (And How to Fix It)

When you're planning a district heating network, there's a deceptively simple question at the heart of every design: How much heating power do you actually need to keep customers warm?

The textbook answer seems obvious: add up the peak heating demand of every building you're connecting, size your pipes and heating center accordingly, and you're done.

Except that's inaccurate. And expensive.

The Problem: Peak Loads Don't Actually Peak Together

Here's what really happens: Building A hits maximum heating demand at 7 AM when everyone showers. Building B peaks at 6 PM during dinner prep. Buildings C and D—both commercial spaces—barely use heat outside business hours.

But at the feasibility stage, planners rarely have measured data. Instead, they assign statistical load profiles by building type—every residence gets the same curve, every office gets the same curve, and so on. All similar buildings peak at the same time in the model, even though real occupants behave differently.

When you sum all those individual peaks, you're designing for a fictional scenario where every building somehow maxes out at the exact same moment. The result? Networks that are systematically oversized by 20-40%, with correspondingly inflated capital costs.

So how do you account for this diversity without measuring every single building? Enter the simultaneity factor¹—a solution validated through decades of empirical research that modern software can now apply automatically.

What Is the Simultaneity Factor?

Think of it as a correction coefficient—a number between 0 and 1 that accounts for the statistical reality that load profiles don't align perfectly. A value of 1 means all buildings peak simultaneously—the theoretical worst case for an investor. A value of 0 would mean no overlap at all, which never happens in practice.

The math is straightforward: as you connect more buildings, the probability that they all peak simultaneously drops. A 50-building network might have a simultaneity factor around 0.8. Scale to 300 buildings? You're closer to 0.5. The loads smooth out.

This isn't guesswork. The relationship has been validated through decades of empirical studies tracking real consumption patterns across residential, commercial, and mixed-use developments.

Simultaneity decreases as buildings are added—allowing smaller pipes and heating systems.²

Why This Changes District Heating Economics

Getting peak load right has cascading effects across your entire project:

Smaller pipes. When your actual peak is 30% lower than your theoretical peak, you can downsize pipe diameters across the network. That's not just cheaper upfront—it's lower heat loss and reduced pumping costs for decades.

Right-sized heating centers. Boilers, heat pumps, and thermal storage all get sized to your adjusted peak load. The capital savings here alone can be substantial, especially on larger networks.

Better ROI. Overbuilding capacity doesn't just waste money—it kills your payback period. Accurate load modeling means your feasibility study actually reflects reality, not a worst-case scenario that will never happen.

How Urbio Built This In

Simultaneity factors have been understood for decades, but applying them in practice required manual calculations and spreadsheet gymnastics to get right, especially for larger projects.

That's the friction we've eliminated: we've made it default, transparent and customizable.

In Urbio, simultaneity calculations run automatically on every network design. You can see the impact visually—adjusted loads displayed directly on network segments, updated heating center sizing in your scenario, and realistic peak demand figures in your reports.

What this means in practice:

• Smaller infrastructure by default - Both pipes and heating centers are sized to actual aggregate demand, not theoretical worst-case scenarios.

• Visual feedback on every segment - Color-coded pipes show where load diversity enables downsizing. Want to understand how exactly it affects your design? Click any pipe for a full breakdown of how simultaneity is reshaping your capacity requirements.

• Customizable when needed - If you need to override it—maybe you're working with an atypical building mix or have specific regulatory requirements—you can adjust the calculation method directly from Urbio's "Building Connections" action card, or use a fixed value. Full control, zero friction.

Network map in Urbio showing pipe segments color-coded by simultaneity factors—darker blues indicate greater load diversity and sizing optimization opportunities.

The Bottom Line

District heating feasibility studies are only useful if the assumptions reflect reality. Summing individual peaks doesn't. Applying a properly calibrated simultaneity factor does.

For planners and investors evaluating hundreds of potential projects across Europe, getting this right at the feasibility stage means better capital allocation, faster payback, and fewer surprises when networks go live.

Want to see how simultaneity factors affect your specific network designs?

👉 Try Urbio for free or talk to our team about getting started.

¹: The simultaneity factor is also known as the "coincidence factor" or, inversely, the "diversity factor".

²: Original data from Winter et al., Euroheat & Power