{"id":117380,"date":"2024-10-17T09:27:39","date_gmt":"2024-10-17T07:27:39","guid":{"rendered":"https:\/\/www.immo-invest.ch\/smarte-gebaeude-fuer-die-zukunft\/"},"modified":"2024-10-17T09:48:18","modified_gmt":"2024-10-17T07:48:18","slug":"smarte-gebaeude-fuer-die-zukunft","status":"publish","type":"post","link":"https:\/\/www.immo-invest.ch\/en\/smarte-gebaeude-fuer-die-zukunft\/","title":{"rendered":"Smart buildings for the future"},"content":{"rendered":"

The city of Schaffhausen has a comprehensive innovation<\/span> programme with its Smart City strategy. As part of the “Smart building<\/span> management” project, it has been working with partners such as VIBOO to find solutions to optimise the management of its buildings. The predictive control technology from VIBOO, an Empa spin-off, offers a future-oriented answer to this challenge. By utilising weather and occupancy data, the heating system can be automatically set to the optimal conditions to both save energy and increase comfort for users.<\/p>\n\n\n\n\n\n

The spin-off uses a combination of machine learning and building physics to create thermal building models based on measurement data. These models are integrated into a predictive control system that predicts the thermal behaviour of the building, taking into account the weather forecast and user preferences. In this way, energy use is optimised every few minutes and adapted to the building.<\/p>\n\n\n\n\n\n

Saving energy in historic buildings<\/strong>
A pilot project in the listed Bach school building demonstrates the potential of this technology. The aim is to reduce heating energy consumption by at least 20% while increasing thermal comfort. The school building, which has energy deficits due to its age, is ideally suited to this type of smart building<\/span> management. Initial results are already available and indicate a significant reduction in energy consumption. If the success is confirmed in the further course of the project, a rollout to other buildings is planned.<\/p>\n\n\n\n\n\n

Integration into the energy sector of the future<\/strong>
Predictive heating systems not only offer advantages in terms of energy savings and comfort, but are also ideally prepared for the future requirements of the energy market. The technology is able to use time-dependent energy prices and can adapt to demand response programmes, which are becoming increasingly important in an increasingly decentralised energy supply with renewable energies.<\/p>\n\n\n\n\n\n

The system aims to fully integrate buildings into the energy sector to reduce peak loads and maximise the use of renewable energy. The vision goes far beyond energy efficiency – buildings should actively participate in the energy market in the future and make an important contribution to stabilising the electricity grid through intelligent networking<\/span>.<\/p>\n\n\n\n\n\n

Validated savings in different building types<\/strong>
The technology has already been successfully tested in residential and commercial buildings, schools and public buildings for heating and cooling. Energy savings of between 20% and 40% have been achieved compared to conventional control systems. These savings are accompanied by improved responsiveness to weather changes, which further increases comfort for building users.<\/p>\n\n\n\n\n\n

With this project, the city of Schaffhausen is setting an example for the use of forward-looking technologies to improve energy efficiency. If the positive results in the Bach school building are confirmed, this could pave the way for the widespread use of this forward-looking technology in other buildings in the city. Predictive heating systems offer a promising solution for significantly reducing energy consumption in existing buildings while increasing comfort – a decisive step towards a sustainable future.<\/p>\n\n","protected":false},"excerpt":{"rendered":"

The city of Schaffhausen is using predictive heating measurement systems to manage its buildings more efficiently. The technology, which is based on machine learning and building physics, promises considerable energy savings and improved comfort. A pilot project in the Bach school building demonstrates the potential of this innovative solution.<\/p>\n","protected":false},"author":2,"featured_media":117365,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"pmpro_default_level":"","inline_featured_image":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[12482,22335,22698],"tags":[],"class_list":["post-117380","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-energy","category-schaffhausen-en","category-sustainability","pmpro-has-access"],"acf":{"bild_mit_ki_erstellt_worden":[],"lead":"The city of Schaffhausen is using predictive heating measurement systems to manage its buildings more efficiently. The technology, which is based on machine learning and building physics, promises considerable energy savings and improved comfort. A pilot project in the Bach school building demonstrates the potential of this innovative solution.","autor":"Quelle: Karin Ibach, Bilder: zVg.","ort":"","bildunterschrift":"","bild_im_grid":false,"gallerie":"","newsim":""},"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t