To effectively reduce building energy consumption and enhance indoor thermal comfort, this study developed a sensitivity analysis and multi-objective optimization method for an office building in Hangzhou considering the effects of parameter interactions. We utilized Latin hypercube sampling (LHS) to sample the parameters and subsequently simulated the energy consumption and discomfort hours for the building’s four orientations. Following this, Gaussian process regression models were established, and the feature importance ranking method (FIRM) alongside an interaction effects method were employed to quantify the sensitivity of 10 parameters. Lastly, a multi-objective optimization was performed for total energy consumption and discomfort hours, yielding a set of Pareto-optimal solutions. Through the VIKOR method, the optimal solution within the Pareto set was identified, resulting in three sets of optimal solutions.
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