Large scale energy labelling with models: The EU TABULA model versus machine learning with open data

In the European Union (EU), the built environment is responsible for 40% of energy consumption. To reduce this energy consumption, policymakers require insight into current efficiency of the building stock and potential for improvement. The EU Energy Performance of Buildings Directive provides guidelines for surveying building performance and assigning energy labels; however, implementation has been slow. To address the backlog, assigning labels based on modelling rather than individual surveying might be an attractive, quicker alternative. The EU TABULA building typologies is such a model; however, in our validation against 2.5 million houses surveyed individually already, the instrument correctly classifies only 26% of buildings. This study proposes that machine learning can help leverage the abundance of (spatial) open data to suggest energy labels more closely matching the quality of individual surveying. Various transparent machine learning algorithms were tested and optimized to select the most suitable technique to model energy labels. This study shows that a random forest classifier trained on open data can reach an accuracy of 71%, thereby demonstrating the potential of open data and machine learning to quickly generate better energy labels for an entire building stock.