Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation

Giacomo Chiesa, Andrea Acquaviva, Mario Grosso, Lorenzo Bottaccioli, Maurizio Floridia, Edoardo Pristeri and Edoardo Maria Sanna
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Giacomo Chiesa: Department of Architecture and Design, Politecnico di Torino, Turin 10125, Italy
Andrea Acquaviva: Department DIST, Politecnico di Torino, Turin 10125, Italy
Mario Grosso: Department of Architecture and Design, Politecnico di Torino, Turin 10125, Italy
Lorenzo Bottaccioli: Department DAUIN, Politecnico di Torino, Turin 10138, Italy
Maurizio Floridia: ICT for Smart Societies, Department DET, Politecnico di Torino, Turin 10138, Italy
Edoardo Pristeri: ICT for Smart Societies, Department DET, Politecnico di Torino, Turin 10138, Italy
Edoardo Maria Sanna: ICT for Smart Societies, Department DET, Politecnico di Torino, Turin 10138, Italy

Sustainability, 2019, vol. 11, issue 11, 1-30

Abstract: Counterbalancing climate change is one of the biggest challenges for engineers around the world. One of the areas in which optimization techniques can be used to reduce energy needs, and with that the pollution derived from its production, is building design. With this study of a generic office located both in a northern country and in a temperate/Mediterranean site, we want to introduce a coding approach to dynamic energy simulation, able to suggest, from the early-design phases when the main building forms are defined, optimal configurations considering the energy needs for heating, cooling and lighting. Generally, early-design considerations of energy need reduction focus on the winter season only, in line with the current regulations; nevertheless a more holistic approach is needed to include other high consumption voices, e.g., for space cooling and lighting. The main considered design parameter is the WWR (window-to-wall ratio), even if further variables are considered in a set of parallel analyses (level of insulation, orientation, activation of low-cooling strategies including shading devices and ventilative cooling). Finally, the effect of different levels of occupancy was included in the analysis to regress results and compare the WWR with corresponding heating and cooling needs. This approach is adapted to Passivhaus design optimization, working on energy need minimisation acting on envelope design choices. The results demonstrate that it is essential to include, from the early-design configurations, a larger set of variables in order to optimize the expected energy needs on the basis of different aspects (cooling, heating, lighting, design choices). Coding is performed using Python scripting, while dynamic energy simulations are based on EnergyPlus.

Keywords: environmental and technological design; passive cooling systems; energy need optimisation; passivhaus; massive simulation modelling; regression analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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