Factory halls, airport terminals and high-rise office buildings are often equipped with automated “anticipatory” heating systems. These systems work with pre-defined scenarios specially calculated for the building and help save building owners a great deal of heating energy. However, such an individual programming is expensive for individual apartments and private homes.

Last summer, a group of Empa researchers proved for the first time that it could be much simpler than that: Intelligent heating and cooling control does not necessarily have to be programmed, the system can just as easily learn to reduce costs by itself based on the data of past weeks and months.

That means programming experts are no longer necessary. That means the cost-saving technology will soon also be available for families and singles.

The experiment took place in Empa’s research building NEST.

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The UMAR unit (“Urban Mining and Recycling”) offers prime conditions for this test: A large eat-in kitchen is framed on both sides by two student rooms. Both rooms are 18 square meters each. The entire window front looks east-southeast toward the morning sun. In the UMAR unit, heated or pre-cooled water flows through a stainless steel ceiling cladding and ensures the desired room temperature. The energy used for heating and cooling can be calculated for each individual room using the respective valve positions.

Since project leader Felix Bünning and his colleague Benjamin Huber did not want to wait for the heating period, they started a cooling experiment in June 2019. The week from June 20 to 26 began with two sunny, but still rather cool days, followed by a cloudy day, finally the sun burned over Dübendorf and drove the outside temperature to over 100.

In the two sleeping rooms, the temperature should not exceed the mark of 77 degrees during the day, at night the limit is set to 73 degrees. A conventional thermostatic valve provided the cooling in one room. In the other room, the experimental control system equipped with artificial intelligence (AI) developed by Bünning and Huber and their team was at work. The AI had been fed with data from the past ten months – and it knew the current weather forecast from MeteoSwiss.

The “Urban Mining and Recycling” unit in the NEST research building has two student rooms. One of them was equipped with a self-learning heating and cooling control system.
Source: Zooey Braun Stuttgart/Empa

The result was clear: The smart heating and cooling control system adhered much more closely to the pre-set comfort specifications – while using around 25 percent less energy. This was mainly because in the morning, when the sun was shining through the windows, the system was cooling the rooms beforehand. The conventional thermostat in the second room, on the other hand, could only react once the temperature went through the ceiling. Too late, too hectic and with full power.

The Empa team has already prepared the next step.

“In order to test the system in a real-world environment, we have planned a larger field test in a building with 60 apartments,” Bünning said. “We will equip four of these apartments with our intelligent heating and cooling control system.”

“I think that new controllers based on machine learning offer a huge opportunity,” Bünning said. “With this method we can construct a good, energy-saving retrofit solution for existing heating systems using relatively simple means and the recorded data.”

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