<abstract xmlns="http://www.w3.org/1999/xhtml">

The energy transition requires optimal knowledge of the thermal behaviour of different passive strategies. This paper explores the impact of 28 variables representing 4 shading devices, 5 external wall compositions (<italic>U</italic>w), 3 window types (<italic>U</italic>w), 4 window-to-wall ratios (WWR), 4 types of climates represented by 4 cities, and 8 orientations. Applying the Latin hypercube sampling method, a campaign of 300 dynamic thermal simulations is performed to assess the impact of the variables selected using the weighted generalised linear regression method for the energy demand for air conditioning, the energy demand for lighting, and the environmental impact expressed in kg of CO2. The model of energy demand for cooling (R² = 0.951) shows that the weather data is the variable that most explains energy demand, followed by the glazing ratio, the thermal characteristics of external walls, and shading devices. The model explaining the energy demand for lighting (R² = 0.945) shows that the WWR and shading devices, the weather data, and the orientation, influence the energy demand for lighting. Finally, the model explaining the embodied carbon footprint (kg of CO2) (R² = 0.989) shows that external walls and window type are the main influencing factors. Finally, the best combination for balancing the cooling-lighting-embodied carbon balance equation is discussed.
</abstract>

The energy transition requires optimal knowledge of the thermal behaviour of different passive strategies. This paper explores the impact of 28 variables representing 4 shading devices, 5 external wall compositions (Uw), 3 window types (Uw), 4 window-to-wall ratios (WWR), 4 types of climates represented by 4 cities, and 8 orientations. Applying the Latin hypercube sampling method, a campaign of 300 dynamic thermal simulations is performed to assess the impact of the variables selected using the weighted generalised linear regression method for the energy demand for air conditioning, the energy demand for lighting, and the environmental impact expressed in kg of CO2. The model of energy demand for cooling (R² = 0.951) shows that the weather data is the variable that most explains energy demand, followed by the glazing ratio, the thermal characteristics of external walls, and shading devices. The model explaining the energy demand for lighting (R² = 0.945) shows that the WWR and shading devices, the weather data, and the orientation, influence the energy demand for lighting. Finally, the model explaining the embodied carbon footprint (kg of CO2) (R² = 0.989) shows that external walls and window type are the main influencing factors. Finally, the best combination for balancing the cooling-lighting-embodied carbon balance equation is discussed.

Impact of Passive Design Strategies on Environment, Cooling and Lighting Energy Demand. A Weighted Least Squares-Based Approach

Selected Scientific Papers - Journal of Civil Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The energy transition requires optimal knowledge of the thermal behaviour of different passive strategies. This paper explores the impact of 28 variables...

Impact of Passive Design Strategies on Environment, Cooling and Lighting Energy Demand. A Weighted Least Squares-Based Approach

Published Online: Dec 29, 2023

Page range: -

DOI: https://doi.org/10.2478/sspjce-2023-0010

Keywords
passive design strategies, cooling and lighting energy demand, Latin hypercube sampling, simulation, weighted linear regression modelling

© 2023 Soufiane Boukarta, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Impact of Passive Design Strategies on Environment, Cooling and Lighting Energy Demand. A Weighted Least Squares-Based Approach

Published Online: Dec 29, 2023

Page range: -

DOI: https://doi.org/10.2478/sspjce-2023-0010

Keywordspassive design strategies, cooling and lighting energy demand, Latin hypercube sampling, simulation, weighted linear regression modelling

© 2023 Soufiane Boukarta, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
passive design strategies, cooling and lighting energy demand, Latin hypercube sampling, simulation, weighted linear regression modelling