Conventional static building envelopes, characterized by fixed thermal and optical properties, cannot adapt to dynamic climatic variations, thereby limiting their carbon-reduction potential in regions with complex climates. Although individual switchable building envelope (SBE) technologies have demonstrated significant energy-saving potential, the synergistic simulation and coordinated control of multi-component SBE systems remain a critical challenge due to nonlinear thermal interactions and heterogeneous control logics. To address this gap, this study proposes a novel co-simulation and optimization framework that integrates three heterogeneous SBE technologies, including a thermally responsive switchable roof, a thermochromic-photovoltaic (PV) switchable window, and a switchable heat-pipe wall, within a unified modeling environment. A high-fidelity platform coupling EnergyPlus and Python is established to resolve hour-by-hour state transitions and dynamic boundary conditions. A single-objective genetic algorithm is then employed to concurrently optimize key thermophysical and control parameters, such as optical transmittances, solar absorptivity, and temperature setpoints, to minimize annual operational carbon emissions. Applied to a typical residential building in Guangzhou (hot-summer and warm-winter region), the synergistic SBE system achieves an annual carbon reduction rate of 48.9%, equivalent to 5.5 kgCO₂/m 2 . Beyond macro-level savings, quantitative temporal analyses reveal a demand-driven regulation pattern: the switchable roof, PV window, and heat-pipe wall autonomously maintain their performance-favorable states (e.g., heat dissipation, shading, and active heat-transfer) for 66%, 29%, and 30% of the year, respectively, with switching frequency proportions below 10.1%. This research provides a robust computational methodology and quantitative insights for the performance-driven design of next-generation climate-responsive building envelopes.
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Wu et al. (2026) studied this question.
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