District energy optimization on a dynamic 8,760-hour foundation
A+Nodes connects building-level dynamic physics to the district network. Instead of aggregated consumption figures, the network is simulated from the actual buildings: return temperatures, simultaneous peak loads and storage behaviour, hour by hour.
Return temperature · Coincident peaks · City-scale digital twins
What static district models cannot see
District energy plans are still routinely built on monthly norms and diversity factors. That approach hides exactly the two things that determine the cost of the network: the hours when every building peaks at once, and the return temperature those hours produce.
A+Nodes starts from the opposite end. Each building is calculated as a dynamic physics model, and those models are then solved together as a network.
Return temperature profile
The district heating return temperature is computed hour by hour from the building-level physics models, not assumed through a diversity factor.
Simultaneous peak load
Real coincident peaks across the district decide production capacity. Aggregated statistics underestimate them; hourly models expose them.
Network and storage sizing
Flow rates, pumping energy, thermal storage and production sequencing are sized against the calculated profile of the whole area.
District-scale batch computation
A 8,760-hour dynamic balance for 30 residential buildings is solved in under five seconds, which makes optimisation iterative instead of annual.
Who the district model serves
Cities and municipalities
A physics-based model of the building stock gives zoning, renovation order and network planning a defensible quantitative base.
District heating utilities
Lower and more stable return temperatures, verified peak loads and storage sizing that is based on the real hourly behaviour of the network.
Developers and consultants
District-level feasibility studies, grant applications and design decisions evaluated before the first pipe is dimensioned.
Each correctly calculated building becomes a node in the district model — see the digital twin value chain.
Model the district, not the average
Send the area, the building stock and the network data you have. We will scope what an 8,760-hour district model would answer in your case.