SwedenReal estate2026 – ongoingSlättö
AI heat curve optimisation in residential buildings
Slättö cut heating energy 13% in an Uppsala apartment building with AI heat curve control, then made it standard across 12 assets
Problem
The heat curve of a building's heating system is usually set by hand and rarely adjusted, so energy is wasted without anyone noticing.
Approach
Slättö compared AI providers on ease of installation, fit for housing and scalability across its portfolio. It then ran a six-month pilot in a 137-apartment building in Uppsala, built in 2018. Temperature sensors went into the apartments, and the Swedish startup Nrlyze used the readings to adjust the heat curve in real time. After the pilot, the approach became standard across 12 residential assets.
Results
Takeaway
Slättö reported a yield on cost above 30% and made the heat controls standard across 12 residential assets. INREV reported the owner's figures without citing independent measurements.
Categories
- Industry
- Real estate
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and MLOptimization
- Adoption
- Vendor solution
- Goal
- Lower cost
- Evidence
- Company's own claim
Sources
Source summaries
INREV, the European non-listed real estate association, describes the Uppsala pilot with Nrlyze using apartment-level temperature sensors. It reports a 13% cut in heating energy, a yield on cost above 30% and no effect on tenant comfort, and says the method is now used across 12 residential assets.
Full details are in the original sources.
