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<https://discovery.ucl.ac.uk/id/eprint/10141464> <http://purl.org/dc/terms/title> "Generating synthetic energy usage data to enable machine learning for sustainable accommodation"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10141464> <http://purl.org/ontology/bibo/abstract> "Machine Learning has the potential to discover new correlations between energy usage in apartments and variables such as seasonality, apartment location, size, efficiency and details of those staying in the apartments, thus helping apartments to become more sustainable and helping those who stay in them to use less energy. The biggest impedance to creating such ML tools is lack of viable data - without the data, the tools cannot be created - yet it is not feasible to wait for several years' worth of good data before creating the tools. Here we present a solution to this problem: the use of a digital twin to generate synthetic data. This approach is viable even when there is no existing data, but when expert knowledge about the relationship between systems exist. To achieve this, we develop a new agent-based synthetic data generator (ASDG) and explore a case study with a corporate housing and luxury alternate accommodation marketplace called TheSqua.re. We show that unlimited quantities of realistic data can be automatically generated, including data for different scenarios, and that it can be used by Machine Learning to discover the underlying correlations."^^<http://www.w3.org/2001/XMLSchema#string> .
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