Enhancing Climate Resilience in Educational Buildings: An MCP-Driven LLM Framework for Shading Optimization Under 2050 Scenarios

Facing 2050 climate uncertainties, enhancing building resilience is critical3. This study addresses the “black-box” and interoperability gaps in traditional multi-objective optimization (MOO) by proposing an intelligent framework based on the Model Context Protocol (MCP) and Large Language Models (LLMs). Unlike stochastic algorithms, the MCP-LLM framework uses semantic reasoning to bridge building performance simulation (BPS) engines like EnergyPlus 24.2.0 and Radiance 5.4. Through a case study of an educational building in Taiwan under the IPCC RCP 8.5 scenario, results show the framework improves optimization convergence speed by 55% compared to NSGA-II. The optimized shading system reduced peak cooling loads by 18.5% and annual EUI by 12.3%, while maintaining uncomfortable glare (DGP > 0.35) below 5% of annual hours. Crucially, the system provides explainable design logic via natural language, marking a shift from automated simulation to human-machine collaboration. This framework offers a transparent decision-support tool for forward-looking climate adaptation in educational environments.