Self-learning digital control of existing properties – test and evaluation

The background of the project is that buildings account for a significant portion of the world's energy consumption, with the greatest potential for energy efficiency lying in existing buildings. The project aims to develop and verify a self-learning AI solution to optimize indoor climate and energy usage in real-time.

The solution offered by the project is the Smart RoomHub, a device that optimizes the indoor climate in each room by adapting existing systems and coordinating their control. This enables both energy efficiency and improved space utilization.

The primary target audience for this solution is property owners who are expected to benefit from lower operating costs, reduced environmental impact, and better space utilization. Additionally, the indoor climate is improved for those who use the premises.

The project is expected to result in quantified and verified energy efficiency in operational areas, leading to lower energy costs and a more stable energy supply. Furthermore, an improvement in indoor climate is expected, which can contribute to increased productivity and well-being among users.

The implementation of the project is structured into three work packages, including the production of a test series of products, installation, optimization of existing control systems, and measurement analysis. A multidisciplinary team with expertise in electronics, data science, and software development leads the implementation. Testing with a prototype is already underway at Vasakronan.

The project runs from July 2024 to February 2025 and includes the production of prototypes, installation and optimization in pilot environments, as well as data analysis and reporting of results.