Quantitative effects of glass roof system parameters on energy and daylighting performances: A bi-objective optimal design using response surface methodology

Abstract

In regions with a hot-summer/cold-winter climate, a balance between energy-saving and better daylighting performance is often required for the design of a glass roof system. This research aims to reduce the total energy demand and to increase the useful daylight illuminance (UDI), by introducing a response-surface-methodology-based bi-objective optimization approach for three glass roof system models (no shading, exterior blinds, interior shades). Surrogate models were generated to quantify the effect of the glazing material and shading parameters. Our findings have revealed that, in hot-summer/cold-winter climate regions like Nanjing, properties of the glazing material could substitute the effect of shading parameters to enhance energy-saving potentials. However, the use of glazing cannot make up for the effect on the daylight performance. Glass roof systems with a low U-value glazing material and small-slat exterior blinds are generally recommended as the balanced solution to lower the energy demand and raise the UDI200–2000. The substitute relations introduced in the surrogate models and optimal solutions have indicated that there are multiple choices to attain a relatively similar goal to produce an energy-efficient building design for construction.

Life Cycle Ecological Footprint Reduction for a Tropical Building

“…erefore, the combination of HCB + LC 3 +multi-Si is best suited for the building in the Indian context. Energy-efficient retrofitting in buildings has great potential to reduce GHG emissions [68,69]. tropical climatic buildings, green roof designs and reflective roofs (reflective coatings on roof surface) can be installed to reduce the ecological footprint of the building [47,66].…”

Optimizing daylight utilization of flat skylights in heritage buildings

“…The study’s results revealed the effectiveness of that glazing technology in Saudi Arabia for being able to reduce annual energy consumption by 19% while maintaining efficient solar radiation providence in space. Yu and Leng [27] optimized the design of a glazed skylight to achieve both the lowest energy consumption and highest daylight performance using three approaches that relied on integrating shades, inner blinds, and exterior blinds systems to the glazed skylight. They used statistical analysis tools such as the analysis of variance (ANOVA), and regression method to uncover the optimal solutions.…”

Applying the response surface methodology to predict the energy retrofit performance of the TABULA residential building stock

“…The RSM method offers an opportunity for building designers to optimize the design response by using a sequence of designed experiments (DOE) that will determine the relationship between input building parameters and the building design response. This approach has been successfully applied to model building consumption for improved energy efficiency in several studies, including EE retrofit optimization of schools [15], university buildings [16,21], office buildings [17], residential dwellings [18,20], and apartment buildings [19]. A summary of the studies utilizing RSM to predict building energy consumption is shown in Table 1.…”

“…Although some of the studies from Table 1 Focused on energy performance prediction in non-residential buildings, they indeed provided a valuable reference for simulation approaches including the selection of potential building parameters for analysis. For example, the RSM simulations of energy consumption in education buildings [15,16,21] and offices [17] identified insulation thickness of the internal [16,21] and external walls [15,21], the heat transmission coefficient of the roofs [15], solar heat gain coefficient (SHGC) [15][16][17]21] and heat transfer coefficient of the external windows [15][16][17]22], roof heat transfer coefficient [16], internal and external shading coefficients [17,21] and window to wall ratio [15] as the key parameters affecting building energy efficiency. Similarly, for residential buildings the RSM simulations [18][19][20] pointed out heating and cooling system set-points [18,20], insulation thickness [18], SHGC [19], air infiltration rate [19], and insulation heat transfer coefficient [18][19][20] as the main contributors to energy savings.…”