Digital Twins for Positive Energy Districts

Section 1

What is a Positive Energy District?

A Positive Energy District (PED) is an urban area that produces more energy than it consumes on an annual net basis, while also delivering environmental and socioeconomic benefits to its residents. PEDs are a cornerstone of the EU's vision for climate-neutral cities, and the European Commission has committed to supporting 100 of them through the NetZeroCities mission.

"Digital twins hold significant potential to overcome the inherent complexities of PEDs — yet their adoption remains limited." — Urrutia-Azcona et al., 2025

The challenge is that PEDs are inherently complex: they span multiple buildings, energy carriers (electricity, heat, gas), governance layers, and stakeholder groups. Conventional planning tools designed for individual buildings are structurally insufficient at district scale.

Why digital twins?

A digital twin is a continuously updated virtual replica of a physical district. It enables:

System-level simulation Real-time monitoring Predictive control Stakeholder reporting

Unlike static models, the twin evolves as the district does; capturing seasonal variation, policy changes, and infrastructure upgrades.

Current barriers

Despite clear potential, digital twinning of PEDs remains rare due to:

Data heterogeneity Platform fragmentation Institutional silos Skill gaps

This research presents a structured methodology to navigate these barriers step by step.

Three stakeholder groups that benefit

Stakeholder	Key need	What the digital twin delivers
City / PED manager	Track progress toward net-zero targets	Live dashboard comparing actual performance to the decarbonisation roadmap
Building / facility manager	Optimise daily operations at building scale	Day-ahead demand forecasts and automated operational recommendations
Policy maker / funder	Evidence for investment decisions and reporting	Scenario comparisons: investment vs. CO₂ saved, costs avoided

Section 2

The 10-step methodology

The methodology organises PED digital twinning into four sequential workflows from initial data collection through to live operational control. Each workflow builds on the previous one. Click any step to read what it involves.

Click any numbered step to read what it involves.

Workflow 1

Digital Twin Setup

1

Data collection

2

Energy modelling

3

Baseline validation

›

Workflow 2

Integrated Design

4

PED-scale scenarios

5

Decarbonisation roadmap

›

Workflow 3

Optimised Operation

6

Cloud engine setup

7

Day-ahead simulations

8

Optimised operation

›

Workflow 4

Results Display

9

3D district environment

10

Operational dashboard

— —

Section 3 · Workflow 1

Building the digital twin

The first three steps establish the foundation on which everything else rests. The quality of the baseline model determines the credibility of every scenario and forecast produced downstream.

Two types of data needed

Static data describes the district as built: building geometry from GIS, energy performance certificates, equipment inventories, network topologies. This typically comes from municipal databases, utility providers, and building owners.

Dynamic data describes the district in operation: hourly or sub-hourly readings from smart meters and IoT sensors for electricity, heating, and cooling. Where live sensors are unavailable, calibrated simulation data can serve as a proxy.

Validation matters

A model is only as useful as its accuracy. The study uses Mean Bias Error (MBE) to measure how closely simulated consumption matches metered data across a full year.

A validated model gives planners and funders confidence that projected savings are realistic (not optimistic assumptions) and that the decarbonisation roadmap will hold up under scrutiny.

Key insight for city procurement: The IES tool suite used in this study integrates three modelling layers: district scale (iCD), network scale (iVN), and building scale (VE), within a single workflow. When evaluating digital twin vendors, verify that district-level and building-level models can exchange data without manual re-entry, as this is where most implementations lose time.

Section 4 · Workflow 2

Planning the decarbonisation path

Workflow 2 is where the digital twin earns its investment in city planning terms. The validated model becomes a policy instrument: it translates proposed interventions into projected CO₂ trajectories, cost curves, and regulatory compliance assessments.

From interventions to roadmap

Energy Conservation Measures (ECMs) are first identified through expert scoping against the local architectural context, existing Climate Action Plans, and available funding instruments. They are then grouped by ambition level and modelled across their implementation timelines.

ECM tier	Examples	Typical disruption	Planning horizon
Shallow	LED lighting, smart controls, behavioural measures	Minimal	1 – 2 years
Deep	Window replacement, rooftop solar PV, heat pump installation	Moderate	3 – 7 years
Full renovation	Envelope insulation, structural upgrades, district energy systems	Significant	8 – 15 years

Illustrative CO₂ trajectories by scenario

The chart below shows how different ECM ambition levels affect a district's annual CO₂ emissions over a 15-year horizon compared to business as usual and to an EU climate target path. Values are illustrative, based on typical proportional reductions for each tier.

Hover any point for exact values. Toggle scenarios by clicking the legend.

The decarbonisation roadmap is not a one-time deliverable. It is a living document. As interventions are implemented, the digital twin tracks actual performance against the projected milestones, enabling the PED manager to detect deviations early and re-plan accordingly.

For policy alignment: The methodology explicitly incorporates existing national and local Climate Action Plans as constraints on scenario design. This means the digital twin output can be directly used to demonstrate compliance with, or progress toward, regulatory targets, streamlining funding applications and progress reporting.

Section 5 · Workflows 3 & 4

Running the district in real time

Workflows 3 and 4 transition the digital twin from a planning instrument to an operational one. Once buildings are configured in the cloud engine, the system runs continuously and automatically with no manual intervention required for routine operation.

Automated daily cycle

Every evening the cloud engine pulls the next day's weather forecast, runs a full building physics simulation for each archetype, and produces a 24-hour demand profile. Facility managers arrive the following morning with a data-driven operational plan already prepared.

Automated at 18:00 daily No manual trigger needed

What the dashboards show

The IES Live platform displays live IoT data alongside simulation forecasts, allowing the PED manager to immediately see whether actual performance is tracking the model, and where deviations are occurring.

Live vs. forecast comparison Building and portfolio views

Stakeholder access model

Both the 3D district environment and the operational dashboards are cloud-based platforms accessible via URL. No software installation or specialist licence required. The study deliberately designed tiered access levels:

User type	What they see	Primary use
PED / energy manager	Full data: live sensors, forecasts, roadmap tracking, alerts	Daily operational decisions
City official / planner	Aggregate metrics, roadmap progress, scenario comparison	Policy review, funding reports
Community / public	Simplified summaries, CO₂ savings, milestone achievements	Transparency and trust-building

Section 6 · Turin Case Study

The Torino PED in practice

The methodology was developed and validated on a real district: the campus of the Politecnico di Torino, recognised as one of the EU's 100 NetZeroCities Mission cities. The case study demonstrates that the full 10-step methodology is feasible in a complex, mixed-use urban environment.

260,000 m²

Total PED area including campus, municipal and residential buildings

3

Energy networks modelled: electricity grid, gas network, and district heating

4

Building categories: educational, research, municipal, and residential

What made Turin a useful test case is precisely its diversity: educational research buildings, municipal offices, university dormitories, private residential buildings, and a district heating network all coexist within the PED boundary. This complexity is typical of European urban districts, and it is exactly the kind of environment where conventional, building-by-building planning tools break down.

User experience research

The methodology included a parallel UX research stream involving structured interviews with two real stakeholders: the energy manager of the Politecnico di Torino campus, and the energy manager responsible for the City of Turin's municipal building portfolio.

Their feedback shaped the design of the dashboards particularly around what information to surface at each access tier, and how to present uncertainty in forecasts to non-technical users.

Replication readiness

The study notes explicitly that the methodology is designed for replication across other European cities. The TIPS4PED project, funded under the European Commission, is already adapting the workflow for additional pilot sites.

Key transferability factors: the use of open GIS data standards, EnergyPlus weather files (freely available globally), and cloud platforms accessible without local infrastructure.

What this means for your city: The Turin validation shows that the 10-step methodology works in a real, legally complex, multi-owner district environment. The primary enabling conditions were: commitment from two key institutional stakeholders to share data, access to the city's GIS database, and metered energy consumption records going back at least one full year. If your city has these, the methodology is deployable.

Conclusions from the research

The study concludes that digital twins represent a structurally different kind of planning tool; one that is multidimensional rather than purely technical. By providing clear, dynamic representations of complex urban energy systems, the platform supports inclusive decision-making and builds trust among stakeholders who would otherwise struggle to engage with raw energy data.

The researchers also highlight two challenges that remain open: data heterogeneity (different buildings use different formats and protocols) and platform modularity (ensuring the methodology works with alternative tool chains, not just the IES suite used here). Both are active areas of ongoing research within the TIPS4PED project.

The paper is published in Open Research Europe (DOI: 10.12688/openreseurope.20962.1) and is freely available under open access terms.

Digital twins for Positive Energy Districts