Surrogate Models for Efficient Multi-Objective Optimization of Building Performance

Abstract

Nowadays, the large set of available simulation tools brings numerous benefits to urban and architectural practices. However, simulations often take a considerable amount of time to yield significant results, particularly when performing many simulations and with large models, as is typical in complex urban and architectural endeavors. Additionally, multiple objective optimizations with metaheuristic algorithms have been widely used to solve building optimization problems. However, most of these optimization processes exponentially increase the computational time to correctly produce outputs and require extensive knowledge to interpret results. Thus, building optimization with time-consuming simulation tools is often rendered unfeasible and requires a specific methodology to overcome these barriers. This work integrates a baseline multi-objective optimization process with a widely used, validated building energy simulation tool. The goal is to minimize the energy use and cost of the construction of a residential building complex. Afterward, machine learning and optimization techniques are used to create a surrogate model capable of accurately predicting the simulation results. Finally, different metaheuristics with their tuned hyperparameters are compared. Results show significant improvements in optimization results with a decrease of up to 22% in the total cost while having similar performance results and execution times up to 100 times faster.