Abstract
The energy design of a building is often an activity of finding trade-offs between several conflicting goals. However, a large number of expensive simulation runs is usually required to complete a Building Performance Optimization (BPO) process with a high confidence of the optimal solutions. Although evolutionary algorithms have been enhanced with surrogate models, complex BPO problems with many design variables still require a prohibitive number of expensive simulations, or lead to solutions with related low accuracy. Hence, performing multi-objective optimizations of actual building designs is still one of the most challenging problems in building energy design. A novel efficient multi-objective algorithm for expensive models based on a probabilistic approach is presented in this work. The new algorithm reduces the computational time needed for the optimization process, while increasing the quality of the solutions found. The algorithm was tested on the optimization problem of three groups of analytical test functions and on the BPO problem related to the refurbishment of three reference buildings. For the latter case, the efficiency, efficacy, and quality of the Pareto solutions found with the proposed algorithm were compared with the true Pareto front previously sought with a brute force approach. The results show that, for the most complex case among the three reference buildings, the algorithm can find about 50 % of the solutions on the true Pareto front with 100 % accuracy. In comparison, other algorithms tested on the same problem and with the same number of expensive simulations, are able to find at best 5 % of solutions on the true Pareto front with an accuracy around 5–10 %.