Building energy optimization using surrogate model and active sampling. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Bamdad, Keivan; Cholette, Michael E.; Bell, John
• Is Part Of: Journal of building performance simulation. Volume 13:Issue 6(2020)
• Page Start: 760
• Page End: 776
• Publication Date: 2020-11-01
• Subjects: Sample selection methods -- artificial neural networks -- building energy efficiency -- surrogate model-based optimization method -- simulation – based optimization method -- meta-heuistic optimization; 690.0113
• DOI: 10.1080/19401493.2020.1821094
• Languages: English
• ISSNs: 1940-1493

Abstract

Abstract : In order to improve the performance of a surrogate model-based optimization method for building optimization problems, a new active sampling strategy employing a committee of surrogate models is developed. This strategy selects new samples that are in the regions of the parameter space where the surrogate model predictions are highly uncertain and have low energy use. Results show that the new sampling strategy improves the performance of surrogate model-based optimization method. A comparison between the surrogate model-based optimization methods and two simulation-based optimization methods shows better performance of surrogate model-based optimization methods than a simulation-based optimization method using the PSO algorithm. However, the simulation-based optimization using Ant Colony Optimization found better results in terms of optimality in later stages of the optimization. However, the proposed method showed a better performance at the early optimization stages, yielding solutions within 1% of the best solution found in the fewest number of simulations.