Partners: Stiftelsen Norsk Institutt for Luftforskning, Norwegian Institute for Air Research
Buildings are vital to our civilization, providing shelter and protection from the environment. However, they also contribute significantly to energy use (40%) and greenhouse gas emissions (36%). Many existing buildings lack sustainability due to historical construction practices predate sustainability concerns for instance energy efficiency, environmental impacts and material conservation. Despite this, they are expected to remain in use by 2050.
The EU aims to improve building energy performance to achieve carbon neutrality by 2050 through initiatives like the “Renovation wave” and “Fit for 55”. Yet, the current rate of energy retrofitting is insufficient, exacerbated by challenges such as the Ukraine invasion and COVID-19 pandemic, hindering progress towards EU climate goals. To improve energy retrofitting rates, a comprehensive approach is essential, considering both broad city/country strategies and detailed building-level analysis. This involves a bottom-up approach, assessing individual buildings to prioritize renovation efforts effectively. UC4 facilitates this process by evaluating residential building energy performance and determining renovation priorities through multi-objective optimization. In addition, UC 4 uses and evaluates the power of machine learning in birding the gaps in data and finding new model to extract new information from the existing data. Some of these activities are data gap filling, classification, and semantic segmentation that UC 4 is testing.
