Performance comparison between KNN and NSGA-II algorithms as calibration approaches for building simulation models
In this paper, a study of calibration methods for a thermal performance model of a building is presented. Two calibration approaches are evaluated and compared in terms of accuracy and computation speed. These approaches are the 푘 Nearest Neighbour (KNN) algorithm and NSGA-II algorithm. The comparison of these two approaches was based on the simulation model of the Birmingham Zero Carbon House, which has been under continuous monitoring over the past five years. Data from architectural drawings and site measurements were used to build the geometry of the house. All building systems, fabric, lighting and equipment were specified to closely correspond to the actual house. The preliminary results suggest that the predictive performance of simulation models can be calibrated quickly and accurately using the monitored performance data of the real building. Automating such process increases its efficiency and consistency of the results while reducing the time and effort required for calibration. The results show that both NSGA-II and KNN provide similar degree of accuracy in terms of the results closeness to measured data, but whilst the former outperforms the latter in terms of computational speed, the latter outperforms the former in terms of results wide coverage of solutions around the reference point, which is essential for calibration.