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viboo – Reducing buildings' energy consumption with predictive control

Redaktion

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Projektdaten

  • Projekt-Nr: GRS-028/22 
  • Förderbeitrag: CHF 150'000 
  • Bewilligung: 28.06.2022 
  • Dauer: 08.2022 - 09.2023 
  • Handlungsfeld:  InnoBooster, seit 2018

Projektleitung

Projektbeschreibung

Heating and cooling of buildings are responsible for a third of the global CO2 emissions. With climate change and emerging discussions regarding energy security and geopolitical conflicts, there is a sincere need to reduce building's energy use. At viboo, we provide self-learning data predictive control algorithms via a Predictive Control as a Service (PCaaS) platform to manufacturers of heating equipment (such as smart thermostats). Our algorithms learn the thermal behavior of a particular building from a single week of measured data and predict how the temperature in the building will evolve in the next couple of hours depending on several factors such as the weather forecast. This allows it to calculate precisely how much heating is required to keep the desired user's comfort. Manufacturers of smart thermostats can use our cloud software service to increase energy efficiency of their products at very low costs, and the end-users can simply book the service through their smart-phone or web app. In our research studies and in pilot projects, one with the market leader Danfoss, we have shown energy savings of up to 20-40%, while improving comfort. The algorithms can also be used to provide electricity reserves (Demand-Side-Managment, DSM) with buildings to allow a higher share of volatile renewables in in the electricity grid.
In this InnoBooster project, we have transformed our research software into a scalable cloud product that can be used by a variety of smart thermostat manufacturers. We have also conducted pilot projects with these manufacturers as well as business and public end-users to further validate the potential of the technology and to demonstrate the integrability with common buildings.

Stand/Resultate

We have transformed our self-learning predictive control algorithm from a research software to a scalable cloud service platform, which hosts all our product features. We have connected several buildings to our platform and verified reliability, scalability, and performance over the course of one heating season. Our platform supports various manufacturers, and we have partnered with national installation companies for a product roll-out in Switzerland. Packages of thermostats, installation and a subscription can now be bought by B2B and B2G customers.

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Am Projekt beteiligte Personen

Felix Bünning, project leadership
Benjamin Huber, project engineer
Nicolas Lefebure, software and development engineer

Letzte Aktualisierung dieser Projektdarstellung  25.01.2024