The urban heat island (UHI) effect significantly impacts building energy consumption, but its effect on energy retrofit potential remains unclear. Here, we propose a data-driven surrogate optimization framework to assess the energy-saving potential of improving building envelope thermal performance in 101 public buildings in Shenzhen. The framework integrates batch building energy modeling, ensemble learning surrogate model training, and multi-objective optimization. The potential changes in building loads resulting from different parameter combinations are defined as energy-saving potential. We use the Urban Weather Generator to adjust typical meteorological year data and account for UHI effects in energy modeling. The results indicate that neglecting the UHI effect in Shenzhen overestimates the potential for building energy retrofitting by approximately 25.2 % (office buildings: -8.96 %, commercial buildings: 60.7 %). Among the nine thermal retrofitting parameters, the heat transfer coefficient of windows contributes the most to the energy-saving potential. Considering the UHI effect or not leads to significant differences in retrofitting strategies and optimal retrofitting parameter configurations. These findings underscore the importance of considering the UHI effect and interactions between buildings in energy retrofit modeling and decision-making processes to formulate precise retrofit strategies and schemes.
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