Assessing the Impact of Energy Retrofits on Indoor Climate Conditions Using Mixed Effects Models: Methodology and R Implementation

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🔬研究者:Methodological reference for causal inference in building performance evaluation using mixed effects models.

🏢実務担当者:Practical guide with R code for analyzing pre/post retrofit indoor climate data.

🏛政策担当者:Supports evidence-based design of retrofit subsidies by providing unbiased impact estimates.

Energy retrofit interventions have become increasingly critical as building sectors worldwide pursue decarbonization targets and improved energy efficiency. However, establishing robust causal inference about retrofit impacts on indoor climate conditions remains challenging due to confounding variables including outdoor climate fluctuations and occupant behavior. This paper presents a methodological framework for analyzing pre- and post-retrofit indoor climate data using linear mixed effects (LME) models, which explicitly account for building-level variability while controlling for environmental and behavioral factors. The approach is demonstrated using a case study analyzing partial pressure of water vapor in Irish residential homes before and after energy retrofit interventions. The analysis incorporates standardized coefficients to assess relative importance of predictive factors and employs model parsimony through stepwise removal of non-significant terms. Complete R code is provided to facilitate adaptation by other researchers. Our results demonstrate that LME models provide unbiased estimates of retrofit effects while avoiding aggregation bias that plague simpler analyses. This paper serves as both methodological reference and practical guide for practitioners seeking to rigorously evaluate building retrofit effectiveness across diverse indoor climate parameters.

• openalex https://doi.org/10.3390/atmos17060560first seen 2026-06-21 05:06:27