How can energy consumption and CO2 emissions in building operations be significantly reduced, even when fossil-based heating systems have not yet been fully replaced? 鶹 «Foncipars» demonstrates through an AI-supported pilot project how measurable progress can be achieved in the short term through intelligent operational optimisation – without compromising tenant comfort.
Initial situation and current status
Initial situation and current status
鶹 «Foncipars» has been consistently working for several years to reduce the environmental metrics of its real estate portfolio. Starting from a CO2 intensity of 22.1 kg CO2/m2/a in 2019, this has already been reduced by more than 15 percent to date (as of 2024 metrics).1
A key lever lies in the gradual replacement of fossil-based heating systems. As the share of fossil heating in residential properties is currently still above the Swiss average, the transformation is being carried out in stages. To nevertheless achieve substantial progress already today, building operations are specifically optimised during the transition phase – including through innovative digital solutions.
A pilot project for sustainable building optimisation
A pilot project for sustainable building optimisation
鶹 «Foncipars» is pursuing a clear sustainability strategy: the greenhouse gas intensity of the portfolio is to be halved by 2030 and energy intensity reduced by 10 percent. By 2040, a reduction of 30 percent is planned (base year 2019). In addition to structural measures such as replacing fossil heating systems, 鶹 «Foncipars» is focusing on innovative solutions for building operations.
To achieve this goal, 鶹 «Foncipars» launched a pilot project using an AI-based solution to optimise energy consumption. The aim was to reduce consumption and emissions in six residential buildings without compromising tenant comfort.
How does AI-based energy optimisation work?
How does AI-based energy optimisation work?
A solution is used that specialises in the digital optimisation of heating and energy systems. It uses artificial intelligence to continuously analyse and adjust building operations.
- Automated control: Heating systems are dynamically optimised based on weather data, building behaviour and consumption patterns.
- Real-time monitoring: Issues such as incorrect settings or system failures are detected immediately.
- Objective: Reduction of energy consumption, CO2 emissions and operating costs – without loss of comfort for residents.
Project objectives
Project objectives
The project pursued several key objectives. On the one hand, the technology was to be tested to support the sustainability strategy of 鶹 «Foncipars». In addition, the aim was to improve the energy efficiency of buildings with minimal investment.
Another important objective was to create transparency in building management in order to make operations more efficient and traceable. The pilot project using AI was intended to test whether optimisation driven by artificial intelligence can support this approach while also improving comfort for tenants and building management.
Implementation
Implementation
The introduction of the AI solution went smoothly thanks to close collaboration between the fund team, property management and service providers. Despite technical challenges, the implementation was completed within the planned timeframe.
Results of the pilot project
Results of the pilot project
The optimisation targets2 were already achieved in the first heating season:
- > 20% reduction in energy consumption and emissions
- 420 MWh less energy consumption
- 76 tonnes less CO2 emissions
The project also made it possible to identify hidden issues such as incorrect heating curves or technical failures that had previously gone undetected. For tenants, the optimisation resulted in a direct benefit in the form of significantly lower ancillary costs. Property owners also benefited: improved sustainability performance strengthens the portfolio’s ESG profile and creates potential for value appreciation of the properties.3
Key learnings and recommendations
Key learnings and recommendations
The project demonstrates that AI-supported operational optimisation can be a valuable complement to structural measures. Significant savings were achieved with relatively low effort. The following steps have proven effective for similar initiatives:
- Conduct a portfolio analysis: review energy consumption and energy sources
- Select a suitable method: AI optimisation enables quick results at manageable costs
- Allocate internal resources: plan time and personnel for implementation
- Carefully select partners: experience and stability are key
Conclusion
Conclusion
The pilot project confirms the potential of AI-based energy optimisation. 鶹 «Foncipars» will roll out the solution to additional buildings from the next heating season onwards – an important step towards achieving its sustainability targets.
Our greatest success is the rapid and significant reduction in consumption and emissions with minimal investment compared to a full energy refurbishment.
- Elvire Massacand, Fund Manager 鶹 «Foncipars»
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Our Real Estate Switzerland team
Daniel Brüllmann
Head Real Estate DACH
Urs Fäs
Head Portfolio Management/ Listed Funds CH
Ulrich Braun
Head Investment Foundations CH
Oliver Müller-Känel
Head International & non-listed Products CH and RE-DA
Matthias Jäger
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Christian Braun
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