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Open AccessJournal ArticleDOI

Optimization of Window Design for Daylight and Thermal Comfort in Cold Climate Conditions

Tony-Andreas Arntsen, +1 more
- 30 Nov 2021 - 
- Vol. 14, Iss: 23, pp 8013
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TLDR
In this article, a typical Norwegian residential building was modeled to investigate whether the criteria and methods facilitate consistent and good performance through different scenario changes and furthermore, how the national regulations compare to European standards.
Abstract
Window design affects the overall performance of a building. It is important to include window design during the initial stages of a project since it influences the performance of daylight and thermal comfort as well as the energy demand for heating and cooling. The Norwegian building code facilitates two alternative methods for achieving a sufficient daylight, and only guidelines for adequate indoor thermal comfort. In this study, a typical Norwegian residential building was modeled to investigate whether the criteria and methods facilitate consistent and good performance through different scenario changes and furthermore, how the national regulations compare to European standards. A better insulated and more air-tight building has usually a lower annual heating demand, with only a marginal decrease in the daylight performance when the window design is unchanged. A more air-tight construction increases the risk of overheating, even in cold climates. This study confirms that a revision of the window design improves the overall performance of a building, which highlights the importance of proper window design. The pursuit of lower energy demand should not be at the expense of indoor thermal comfort considering the anticipated future weather conditions. This study indicates that criteria for thermal comfort and daylight, if clearly defined, can affect the energy demand for heating and cooling, as well as the indoor climate positively, and should be taken into account at the national level. A comparison between the national regulations and the European standards was made, and this study found that the results are not consistent.

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Citations
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Journal ArticleDOI

Optimizing Window Configuration Counterbalancing Energy Saving and Indoor Visual Comfort for Sydney Dwellings

TL;DR: In this paper , a multi-objective optimization using NSGA-II considering visual and thermal comfort along with energy usage and view of the outside is proposed to optimize sunlight admission and maintain indoor comfort while minimizing energy consumption.
Journal ArticleDOI

Predicting annual illuminance and operative temperature in residential buildings using artificial neural networks

TL;DR: In this article , a fully connected neural network is developed with five hidden layers and five different neuron structures to predict both annual daylight illuminance and operative temperature, in order to reduce the overall simulation time.
Proceedings ArticleDOI

A study on multicollinearity of design variables of office buildings’ standard floor regarding daylight and thermal performance

TL;DR: Wang et al. as mentioned in this paper used principal component analysis (PCA) to solve the multicollinearity problem that 15 design variables including a building's body (B, DR, FH, C and RE), window form (WWR, WH, SH, WHD and WVD) and envelope property (K, SHGC, VT, EW_R and F_R) were reduced to 12 and divided into four principal components (PCs), namely PC1 (building's appearance), PC2 (window position), PC3 (window property) and PC4 (exterior-wall property), and then weights of PC1~PC4 were obtained by weight analysis to be 0.41, 0.188 and 0.134 respectively.
References
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Journal ArticleDOI

Influence of occupant behavior and operation on performance of a residential Zero Emission Building in Norway

TL;DR: In this article, the authors evaluate the change in the energy performance of a zero emission building (ZEB) located in Norway and find that occupant behavior may significantly change building energy performance.
Journal ArticleDOI

Impact of Building Design Parameters on Daylighting Metrics Using an Analysis, Prediction, and Optimization Approach Based on Statistical Learning Technique

TL;DR: This article will outline how this methodology works, and analyze the building design features that have the strongest impact on daylighting performance, and create statistical models to predict daylighting metric values for different building types and design solutions.
Trending Questions (2)
How can the shape of a residential building in cold climate be designed to optimize heating and daylight?

The paper does not provide specific information on how the shape of a residential building in a cold climate can be designed to optimize heating and daylight.

What are the challenges of designing residential buildings in cold climates with the approach of heating and daylight optimization?

The paper does not explicitly mention the challenges of designing residential buildings in cold climates with the approach of heating and daylight optimization.