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[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 Elsevier LtdDesign optimisation problems of window size in buildings with regard to energy saving and comfort criteria have been investigated many times. To indicate daylight availability and energy consumption in indoor spaces, a number of metrics have been proposed, but so far there is no convention on which daylight and energy metrics are preferred. Meanwhile, evolutionary techniques such like genetic algorithm have long been used to optimise parameters in building design. In the optimisation process, however, different metrics or objectives normally lead to different degrees of uncertainty of the obtained results. This article presents a study to determine the most appropriate metrics for the case of daylight optimisation in a reference office space, by comparing various daylight metrics and lighting energy demand indicators, using genetic algorithm to optimise the window-to-wall ratio (WWR) and the room interior reflectance. To determine the appropriate metrics, the optimisation results were classified based on their computational precision. It is found that maximising spatial useful daylight illuminance (sUDI)100∼2000lx,50% − sUDI>2000lx,50% leads to objective function values with the highest precision, while minimising annual lighting energy demand + sUDI>2000lx,50% gives the most robust input variables. Therefore, these two pairs of metrics are suggested as the most appropriate for optimising daylight in the particular space.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Computational precision,Daylight availability,Energy demands,Evolutionary techniques,Objective function values,Optimisations,Useful daylight illuminance,Window-to-wall ratio[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Daylight metrics,Genetic algorithm,Lighting energy demand,Optimisation,Room reflectance,Window-to-wall ratio[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This research was supported by the Institute of Research and Community Service of Institut Teknologi Bandung (LPPM ITB), through the P3MI ITB 2018 Research Program. We gratefully acknowledge the funding from USAID through the SHERA program – Centre for Development of Sustainable Region (CDSR).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.solener.2018.06.025[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]