Scaling solutions across portfolios
AI-optimised residential heating
AI-driven solutions are entering the market with compelling claims. Before rolling out AI-driven heating optimisation across its portfolio, Slättö carried out a structured pilot to prove the business case.
Intervention
Following an assessment of several AI providers, Slättö selected Nrlyze for a six-month pilot at a newly built residential asset in Uppsala. Temperature sensors were installed in every apartment, enabling the system to optimise the building's heat curve in real time based on actual indoor conditions.
Throughout the pilot, energy consumption, indoor climate and operational performance were continuously monitored to evaluate both the business case and the potential for wider portfolio deployment.
From pilot to programme
The pilot reduced the average heat curve by up to 9°C while maintaining tenant comfort. Based on these results, AI-driven heating optimisation has become a standard energy improvement measure for Slättö's newly built residential portfolio.
The solution is currently being rolled out across 12 residential assets with a total investment of approximately EUR 250,000, generating expected annual energy cost savings of around EUR 50,000 and an estimated Yield on Cost of 20%.
Impact
AI in residential heating: Uppsala asset
| Key results | |
| Energy capex, EUR | 27 k |
| Net Operating Income, EUR | +10 k |
| Yield on Cost | 37% |
| Heating energy cost | -13% |
| Asset overview | |
| Location | Uppsala |
| Asset type | Residential |
| Number of apartments | 137 (two buildings) |
| Construction year | 2018 |
| Heating system | District heating |
Takeaway
AI can reduce heating energy in residential. If carefully designed, successful pilots can become repeatable programmes rather than isolated projects.