Probabilistic LCA and LCC to identify robust and reliable renovation strategies

A Galimshina,S Lasvaux,A Hollberg,M Moustapha,G Habert,D Favre,B Sudret,P Padey

doi:10.1088/1755-1315/323/1/012058

Abstract

Buildings are one of the largest energy consumers and greenhouse gas emitters in the world. As the largest part of the energy consumed by the existing non-insulated buildings occurs during the operation stage, retrofitting the building stock is crucial to reduce the environmental impact. To guarantee that the retrofit measures provide economic and environmental benefits, the whole life cycle should be assessed. However, the identification of environmental and at the same time cost-effective solutions is difficult due to the complexity and the uncertainty involved. Currently, simplified approaches based on limited assumptions are used that can lead to inaccurate results. This paper proposes a method for identifying robust renovation scenarios for residential buildings in Switzerland. The method and the developed tool use 47 uncertain parameters and Sobol’ indices to identify the most influential parameters. As such, robust renovation strategies can be identified in the early design stage.

Highlights

Buildings are the largest energy consumers in the world [1]
A statistical method combined with existing Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies was applied to the renovation scenarios of one residential building in Switzerland
Once the heating system is fixed, the level of uncertainty on LCA and LCC is much reduced allowing to focus on the renovation of the building envelope

Summary

Introduction

Buildings are the largest energy consumers in the world [1]. The largest part of the energy consumption of existing buildings occurs during the operation stage. The conventional LCA and LCC approaches apply deterministic assumptions for many parameters These assumptions are taken for the input model parameters (e.g. material properties, selected material costs and environmental impacts) as well as exogenous parameters, which cannot be affected by the designer but directly impact the model response (e.g. room temperature, external climate, discount rates, price growth rates).

Methods

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Discussion

Conclusion