Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: Background, methodology, setup, and machine learning results. (August 2021)

• Record Type: Journal Article
• Authors: Ekici, Berk; Kazanasmaz, Z. Tuğçe; Turrin, Michela; Taşgetiren, M. Fatih; Sariyildiz, I. Sevil
• Is Part Of: Solar energy. Volume 224(2021)
• Page Start: 373
• Page End: 389
• Publication Date: 2021-08
• Subjects: Performance-based design -- Building simulation -- Sustainability -- High-rise building -- Machine learning -- Optimization; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2021.05.083
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: High-rise design in dense urban areas is addressed using artificial intelligence. Part 1 presents background, methodology, setup, and machine learning results. Two design scenarios focus on daylight metrics using parametric modelling. 31 models reported very high and 9 models reported high prediction accuracies. The importance of surrogate modelling is discussed for sustainable metropolises. Abstract: Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings, previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However, different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings, as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase.