Sustainable design requires holistic decision making already in the feasibility stage. One critical aspect of a buildingś sustainability is its operational energy consumption, but energy simulations typically are too time‐consuming for early, fast‐paced design phases. Data‐based, Machine Learning models – so called surrogates – can replace time‐consuming simulations with real‐time estimates. This paper investigates the accuracy of different surrogate models for energy performance by comparing different types of models and numbers of samples. The paper also presents an integrated dashboard for holistic decision making as an application of the developed surrogate.

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