eprintid: 10111638 rev_number: 12 eprint_status: archive userid: 608 dir: disk0/10/11/16/38 datestamp: 2020-10-07 13:51:35 lastmod: 2020-10-07 13:51:35 status_changed: 2020-10-07 13:51:35 type: proceedings_section metadata_visibility: show creators_name: Yu, C creators_name: Altamirano, H title: Generating optimal comfort improving design solution with occupancy survey and Multi-Objective Optimization (MOO) technique: a case study for façade retrofit in a post-war office in London ispublished: pub divisions: UCL divisions: A01 divisions: B04 divisions: C04 divisions: F34 keywords: Multi-objectives optimization, Occupancy comfort, Post-occupancy survey, Façade retrofit, Building performance simulation note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: In a UK post-war highly glazed open-plan office, occupants experienced intolerable discomfort from poorly designed and performing façade. However, the comfort-oriented solution was limited to internal of the window only by rental restriction. No direct performance data, e.g. monitoring data, was available for analysing potential discomfort issues. Such case presented a challenge for a balanced optimal solution for undefined but potentially compilated causes for problems. This essay developed a practical and systematic framework to identify key comfort issues with occupancy survey and generate an optimized design option with multi-objective optimization with genetic algorithm for the case office. Benchmarking, correlation and ANOVA study were cross-referred and integrated with non-linear satisfaction theory to determine the key comfort factors. In this case, the key comfort factors were identified as direct sunlight glare, temperature and stability in winter and summer, and noise from colleagues. These key comfort factors were parameterized with building simulation programs and set as objectives in optimization program thus integrating the qualitative survey into quantitate optimization algorithm for design options optimization. Glazing ratio, shading device length, and secondary glazing were set as changeable parameters (genes) controlling the façade characteristics. The optimization program generated and compared 480 distinctive design options through 8 generations and discovered 101 Pareto front options. Filtered with a criteria-based filtering method from all pareto front, an optimal solution was recognized as fully opaque insulation for South and West façade, high glazing ratio on North and East façade, no extra shading device, and with secondary glazing. This optimal option effectively reduced glare under acceptable threshold while keeping large view out but only slightly improve summer thermal condition. Further improvements in ventilation system and building envelope performance was demonstrated essential for significant thermal comfort improvement. date: 2019-09-20 date_type: published publisher: MC2019 Masters Conference People and Buildings official_url: http://nceub.org.uk/ocs/index.php/MC2019/MC2019 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1774591 lyricists_name: Altamirano, Hector lyricists_id: HALTA34 actors_name: Altamirano, Hector actors_id: HALTA34 actors_role: owner full_text_status: public place_of_pub: London, UK event_title: NCEUB Conferences, MC2019 Masters Conference People and Buildings institution: NCEUB Conferences, MC2019 Masters Conference People and Buildings book_title: Proceedings of the MC2019 Masters Conference People and Buildings citation: Yu, C; Altamirano, H; (2019) Generating optimal comfort improving design solution with occupancy survey and Multi-Objective Optimization (MOO) technique: a case study for façade retrofit in a post-war office in London. In: Proceedings of the MC2019 Masters Conference People and Buildings. MC2019 Masters Conference People and Buildings: London, UK. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10111638/1/MC2019_Yu_Chuanrui_v2.pdf