A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios. (15th November 2017)

• Record Type: Journal Article
• Authors: Chen, Xi; Yang, Hongxing
• Is Part Of: Applied energy. Volume 206(2017)
• Page Start: 541
• Page End: 557
• Publication Date: 2017-11-15
• Subjects: Surrogate model -- Green building -- Energy demand -- Passive design -- Machine learning; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.08.204
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A multi-stage design optimization approach is developed for passive building design. Multiple machine learning methods are applied to develop surrogate models. Computation efficiency of NSGA-II algorithm is greatly improved. Design optimization is applied to different ventilation and thermal load conditions. Applicability of passive design optimization in more diverse climate is studied. Abstract: This article proposes a two-stage design optimization approach which is applied to a prototype passively designed high-rise residential building under different ventilation modes and thermal load requirements. Machine learning methods are employed to develop surrogate models for improving the computation efficiency of the multi-objective optimization process. The surrogate model is trained by modeling experiments with EnergyPlus and R, which can provide reliable energy performance indicators of generic building models featuring passive design parameters including the building layout, envelope thermophysics, building geometry and infiltration & air-tightness. The lighting, cooling and heating demands of the generic building model are determined by the hybrid ventilation and light diming control strategies in compliance with local green building assessment criteria in Hong Kong. The multiple linear regression (MLR), multivariate adaptive regression splines (MARS), and support vector machines (SVM) are examined by the statistical modelling. SVM is capable of fitting a surrogate model with the best prediction performance based on the coefficient of determination and root mean square error. In addition, both single-sided ventilation and cross-ventilation models under varied thermal load requirements are investigated to compare the preferable design solutions for each scenario. The multi-stage optimization approach is also applied to a Mediterranean climate to explore optimal design solutions in more diverse external environmental conditions. This research can provide a highly efficient design optimization tool to appropriately deploy passive architectural strategies in a green building project.