A self-learning algorithm that helps save heating energy

A thermostat that predictively controls the indoor climate and thereby improves energy efficiency and comfort—Empa researchers Felix Bünning and Benjamin Huber came up with this idea while working in Empa’s Urban Energy Systems lab. They developed a control algorithm that can calculate a building’s ideal energy use several hours in advance based on weather forecasts and building data. The first experiments at NEST, Empa’s and Eawag’s research and innovation building, showed that this approach can save around 25% of the energy.

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