Abstract

The construction of low greenhouse gas emission buildings requires the consideration of both embodied and operational emissions. While embodied emissions mainly occur in the construction phase, the operational emissions occur during the entire building life and are influenced by future developments such as climate change, electricity grid decarbonization, and user behavior. Suitable methods which allow considering these uncertainties already during the design stage are robustness assessments.This study proposes a scenario-based robustness assessment method to investigate the effects of future developments on buildings’ greenhouse gas emissions. We investigate the scenario generation, building energy simulation, calculation of embodied and operational emissions, and the statistical evaluation of the results according to different robustness metrics. Two simulation models with different time scales are compared with a total of 12 test cases. Finally, the robustness assessment is demonstrated on a case study, where a total of 40 configurations against 1260 scenarios are investigated using five different robustness metrics: minimax regret, Spread, Maximin, Laplace’s Principle of insufficient reasoning, and Starr’s Domain criterion.For the case study, a multi-family house located in Zurich, Switzerland, we demonstrate how future developments, e.g., climate change and electricity grid decarbonization, influence the building's greenhouse gas emission performance. For example, with slow decarbonization, heat pump systems perform most robustly compared to the other analyzed heating and cooling systems. Depending on the chosen robustness metrics, it is further possible to rate the solutions according to optimistic, pessimistic, or optimal preference, making the proposed framework a valuable tool for decision-makers.In a time of transition, we conclude that analyzing building performance without future-oriented data is not suitable, and we encourage building designers to assess the impact of possible future scenarios in the design phase instead of purely using norm-based standard values, entirely based on data from the past.

Highlights

  • Similar to the previous analysis, we investigate the impact of fixing the climate change scenario on the robustness assessment

  • We presented a scenario-based robustness assessment method, compared two simulation models with different time scales and five robustness metrics

  • Simulation results were compared with a total of 12 test cases, and the robustness assessment was demonstrated on a case study

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Summary

Motivation

Buildings largely contribute to global greenhouse gas emissions, and it is undisputed that the building sector must be entirely decarbonized by the latest 2050 with ambitious goals already for 2030 [1]. The fraction of embodied emissions in buildings increases and reaches almost 50% in technologically advanced office and residential buildings [6] For this reason, it is crucial to investigate zero-carbon instead of zeroenergy buildings and shift towards a life cycle perspective to investigate the building’s contribution to global decarbonization [7]. Global warming will affect future building performance, which makes planning on historical weather data critical It is, crucial to include uncertainty in building energy assessments and to investigate the robustness of building energy systems against future conditions [9,11,12,13]. We need buildings that perform well under a set of diverse yet plausible futures with minimal carbon emissions in the construction phase

State of the art of robustness analysis for low-energy buildings
Identification of probable future scenarios
Performance simulation
Scenario-based robustness assessment
Framework overview
Identification of configurations
System analysis framework
Building configurations
Occupant preferences and behavior
Decarbonization
Scenarios
Climate change
Electricity and DHW demand
Energy supply models
System models
PV self-consumption
Impact of operational emissions
Analysis period
Impact of embodied emissions
Energy demand
GHG emissions
Robustness assessment using a case study
Case study configurations
Robustness assessment
Case study scenarios
The influence of boundary conditions on the robustness
Hourly versus monthly model
Findings
Robustness metrics and results
Summary and conclusion

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