TSE Smart & Grid Interactive Offices

Using data to make buildings smarter and gain flexibility from offices

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. 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, with various use cases when it comes to heating techniques, electrical vehicle charging, energy storage, et cetera.

PARTNERS Huygen Ingenieurs & Adviseurs B.V., Merin , Greenchoice

DATE January 2021 – December 2023

Challenge

More strict legislation, transitioning away from natural gas, and higher demands by users have led to bigger investments in sustainable energy technology in offices. The number of solar panels, ATESes, heat pumps, EV charging points and similar installations will rise significantly in the coming years. The current methods of control are not very dynamic, and there is no smart interaction between these installations or with the grid. Because of that, a huge potential for energy savings and flexibility remains structurally unused. This leads to inefficient office buildings with unnecessarily high energy costs, a suboptimal indoor climate, unsatisfied users, and broader challenges when it comes to energy infrastructure.

Approach

The consortium wants to unlock offices’ (so far underused) energy savings and flexibility potential while simultaneously realizing higher user comfort. We plan to do so by using a dynamic, self-learning control system based on machine learning algorithms and digital simulations. This system is connected to the buildings in real-time and continuously looks for the best possible use of building installations. The control system is also designed to optimize self-consumption. Thanks to a connection with the energy markets, it can also respond to the fluctuations in supply and demand associated with the structural increase in sustainable energy generation in the Netherlands.

Outcome

The result is a self-learning, autonomously controlling measuring and control system that is aimed at the active and real-time control of installations to optimize energy efficiency and comfort. The system can save on average at least 20% energy, capitalize on flexibility, increase self-consumption and increase the comfort of users. It does this in such a way that the business case for local generation and storage improves and the end-users also benefit. The technology developed can be applied in concrete terms as an upgrade for existing office buildings in the future. In this way, these buildings are made future-proof in an affordable way in view of the sharp increase in supply and demand of electricity in the built environment and the associated challenges for the power grid. In addition, the project results in technology for realistic simulation of energy-saving measures in a building, in order to contribute to the energy transition in a broader sense.

Geert Litjens

Energy Consultant

Any questions?

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