AI District Heating Optimization

Machine learning for district heating network efficiency and control

The Problem

“Cut district heating costs amid volatile demand”

Organizations face these key challenges:

Inaccurate demand forecasts cause overproduction, higher return temperatures, and wasted fuel/heat losses

Complex multi-asset dispatch (CHP, heat pumps, boilers, storage) under volatile electricity prices and emissions constraints is difficult to optimize manually

Network constraints (ΔT, pressure, supply/return limits) and customer comfort requirements lead to conservative setpoints and inefficient operation

Impact When Solved

3–10% lower operating cost via optimized dispatch and thermal storage scheduling5–15% CO2 reduction by prioritizing low-carbon heat sources and reducing losses20–40% fewer temperature/pressure violations and improved service reliability

The Shift

Real-World Use Cases

Smart Grid Management and Optimization

A smart grid is like upgrading from an old landline to a modern smartphone for your electricity network. Instead of just pushing power one way from big plants to homes, the grid becomes two‑way, with sensors and software that can see what’s happening in real time, shift loads, use home batteries and solar panels, and prevent or shorten outages.

Time-SeriesEmerging Standard

9.0

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.