Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 2: Optimisation problems, algorithms, results, and method validation. (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: 309
• Page End: 326
• 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.2021.05.082
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Part 2 presents optimisation problems, algorithms, results, and method validation. Three optimisation algorithms are employed with five replications for two scenarios. Results are obtained in 4 days instead of 17 years using surrogate models of Part 1. Optimised designs outperformed 8748 and 5832 regular high-rise scenarios. The importance, potential, and optimisation with surrogate models are discussed. Abstract: High-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design; thus, the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs), which provide a swift and accurate prediction for the entire building design; hence, the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process, the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices, glazing type, and building-shape parameters using the setup, and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology, optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios, respectively, using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas.