Qualitative and quantitative optimization of skylights : a comprehensive and inclusive analysis of skylight sizes for an office while providing enough daylight, avoiding glare and saving energy

While windows connect inside to outside, daylight entering through windows is a key element in architectural design. Although electrical lighting is able to replace daylight as an essential lighting requirement, daylight has qualitative and quantitative aspects that distinguish it from its competitor, electrical lighting. One of the most unique characteristics of daylight is its variability in time, including different qualities of daylighting from sunset to sunrise, and from equinox to solstice. In addition, by regulating a circadian rhythm and hormone secretion, daylight impacts the physiological and psychological well-being of human beings. Moreover, daylight through windows carries information that flows from outside to inside and makes occupants aware of the outside world. While availability of daylight has been praised in building design, uneven distribution of daylight, reflective surfaces and excessive daylight may cause glare issues and visual discomfort which need to be avoided in daylight design. Beyond all the qualitative aspects of daylight, daylight, as a free resource, is able to illuminate the space and replace electrical lighting and lower electricity utility bills. This quantitative aspect of daylight has been the center of attention among researchers, designers and builders, as lowering CO₂ emissions and environmental design have gained momentum in the building industry. Different stakeholders have various interests in qualitative and quantitative aspects of daylight, which eventually shape the design context. The interests of different stakeholders, including owners, environmentalists and occupants, may merge or conflict in different projects, which shows that daylight quality and quantity may have different weights, depending on the context of the project at hand. This dissertation aims to provide an algorithmic platform to consider a context for skylight design by including all the interests of different stakeholders while either scaling importance of the different interests or requiring minimum qualities and performance targets. This dissertation proposes different methodological approaches for its platform to include both qualitative and quantitative aspects in designing skylights for a one-storey office building in different climates. Three different approaches are proposed in this dissertation, encompassing unconstrained optimization, constrained optimization and monetary metrics. In the unconstrained optimization approach, the algorithmic platform has been developed to implement Parametric Analysis (PA) and Gradient Descent (GD) methods in order to optimize Skylight to Floor area Ratio (SFR) while saving energy consumption, as a quantitative aspect of daylight, and improving daylighting quality by providing sufficient daylight without causing glare discomfort. This platform was built as an Inclusive Integrative Algorithm (IIA) to weight different qualitative and quantitative aspects of daylight. The algorithm is able to perform single or multi-objective optimization by either applying GD or PA. In this approach, a single-objective optimization, considering only energy efficiency, showed that the optimal SFR was 6% in the examined climates of Austin, Chicago and San Francisco, for 300 lux lighting level and Lighting Power Density of 0.8 watt/sqft. The unconstrained optimization approach implemented a weighting system for an aggregated metric, including Mean Daylight (MD) and imperceptible Daylight Glare Probability (iDGP) and Ratio of Energy Saving (RES), which resulted in a SFR of 11% as the inclusive optimal solution for all the examined climates. In addition to the discussion of inclusive optimization considering both daylight and energy performance and scaling their importance, this dissertation initiated the use of GD for the unconstrained optimization in single and multi-objective optimization. The result showed that GD is considerably faster than the traditional method, PA, while predicting the optimal solution with higher resolution. For example, GD resulted in 6.22% SFR for the San Francisco climate as an energy efficient optimal solution by only 9 iterations. However, PA required 10,000 iterations to find the optimal solution with the same resolution. Thus, GD has shown a promising result for the future of multi-objective optimization in building design. In addition to the unconstrained optimization, this dissertation applied the second approach, constrained optimization, by imposing different thresholds for two sets of metrics, including daylight availability and glare. Where Useful Daylight Illuminance (UDI) and spatial Daylight Autonomy (sDA) of 100% were used, the inclusive optimal SFRs were 9-10%, 8-10% and 9% for the climates of San Francisco, Austin and Chicago, respectively. For the other set of daylight metrics, MD of 50% and Mean Daylight Glare Probability (mDGP) of 35% were used, which resulted in optimal solutions of 7-14%, 7-11% and 8-13% SFR for San Francisco, Austin and Chicago, respectively. Therefore, multi-objective optimization considering both daylight and energy performance resulted in different inclusive optimal solutions to energy optimization alone. The study also concludes that optimal solutions depend on applied metrics and daylight thresholds. For the third approach this research investigated the monetary gains from energy efficiency and increased productivity. Assuming that productivity does not occur in spaces with poor daylight performance, inclusive optimal solutions will be the scenarios that most probably boost productivity. The study indicated that the energy cost saving is always negligible compared to the monetary gains from minimum increased productivity (1%). This conclusion may influence an owner’s perspective toward the quality of daylight performance and its resultant productivity increase. Although the proposed algorithm (IIA) has been used to perform multi-objective optimization for skylight design, this platform can be used in the design process to optimize any fenestration, including widows, based on daylight availability, glare and energy factors. GD as one of the contributions of this dissertation is a faster and more accurate method which can facilitate the application of multi-objective optimization for daylight analysis in the early stage of design.