In this project, a consortium researched, among others, building users’ comfort, thermal capacity, energy usage, flex potential, and the degradation of installations to gain insights into smart control of office buildings. These insights were translated into an integral building model and used to develop a digital simulator. Using the simulator, a building’s “behaviour” can be predicted. This building intelligence has since been incorporated into our software’s artificial intelligence. This complex whole of self-learning algorithms uses input like realtime building data, weather data, and energy prices to integrally weigh the best decision for building control. Using the developed algorithm, the consequences of various control strategies are compared and optimized.

We develop affordable and robust hardware to physically measure and control an office building. In this project, both the software and the hardware were tested in five pilot locations.

These insights were translated into an integral building model and used to develop a digital simulator. Using the simulator, a building’s “behaviour” can be predicted. This building intelligence has since been incorporated into our so

ftware’s artific

ial intelligence. This complex whole of self-learning algorithms uses i

nput like realtime building data, weather data, and energy prices to integrally weigh the best decision for building control. Using the developed algorithm, the consequences of various control strategies are compared and optimized. At the same time, we develop affordable and robust hardware to physically measure and control an office building. In this project, both the software and the hardware were tested in five pilot locations, all office buildings owned by Merin.

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