Early-stage design support combining machine learning and building information modelling

Global energy concerns necessitate designing energy-efficient buildings. Many important decisions affecting energy performance are made at early stages with little information. Dynamic simulations support informed decision-making; however, uncertainty, high computational time, and expensive modelling efforts impair their use at early stages. This article develops an approach using building information modelling and machine learning that provides quick energy performance information. This approach has been implemented into a web tool, p-energyanalysis.de. It allows design space exploration, assesses the energy performance of design options, compares multiple options, performs sensitivity analysis, and tracks changes. Twenty-one participants (researchers and architects) used it as a support tool for designing an energy-efficient building. Their feedbacks are discussed as part of the tool development. The study found that the tool supports early-stage design decisions by quickly providing relevant information. The limitations, such as the bias in the results towards training data population and implementation issues, are also discussed.