AD-Based Surrogate Models for Simulation and Optimization of Large Urban Areas

Abstract

Urban Building Energy Model (UBEM) approaches help analyze the energy performance of urban areas and predict the impact of different retrofit strategies. However, UBEM approaches require a high level of expertise and entail time-consuming simulations. These limitations hinder their successful application in designing and planning urban areas and supporting the city policy-making sector. Hence, it is necessary to investigate alternatives that are easy-to-use, automated, and fast. Surrogate models have been recently used to address UBEM limitations; however, they are case-specific and only work properly within specific parameter boundaries. We propose a new surrogate modeling approach to predict the energy performance of urban areas by integrating Algorithmic Design, UBEM, and Machine Learning. Our approach can automatically model and simulate thousands of building archetypes and create a broad surrogate model capable of quickly predicting annual energy profiles of large urban areas. We evaluated our approach by applying it to a case study located in Lisbon, Portugal, where we compare its use in model-based optimization routines against conventional UBEM approaches. Results show that our approach delivers predictions with acceptable accuracy at a much faster rate.