In the process of building design, energy performance is often simulated using physical principles of thermodynamics and energy behaviour using elaborate simulation tools. However, energy simulation is computationally expensive and time consuming process. These drawbacks limit opportunities for design space exploration and prevent interactive design which results in environmentally inefficient buildings. In this paper we propose Energy Model Machine (EMM) as a general and flexible approximation model for instant energy performance prediction using machine learning (ML) algorithms to facilitate design space exploration in building design process. EMM can easily be added to design tools and provide instant feedback for real-time design iterations. To demonstrate its applicability, EMM is used to estimate energy performance of a medium size office building during the design space exploration in widely used parametrically design tool as a case study. The results of this study support the feasibility of using machine learning approaches to estimate energy performance for design exploration and optimization workflows to achieve high performance buildings.
As building information modelling (BIM) software becomes ever more powerful, how will the architect's role be affected? Ian Keough and Anthony Hauck of the AEC Generative Design group at leading software corporation Autodesk present their vision. They argue that the value of building professionals' expertise in advising clients on priorities and choices has never been higher. BIM offers greater guarantees of structural integrity and constructional feasibility, and scalable cloud computing allows numerous factors to be explored simultaneously; but success is only assured if the right parameters are set.
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