AutoEnergyDesign: An LLM-Driven Agent for Automatic Energy System Design Optimization

AutoEnergyDesign is an LLM‑driven framework that optimizes building energy system designs by iteratively proposing configuration changes based on prior simulation results and automatically reverting unsuccessful changes via a git‑based mechanism. The agent combines a reasoning model with explicit memory (procedural, semantic, episodic, reflective), a constrained action interface, and a keep/revert signal, and evaluates designs using a 25‑year Net Present Cost model across eight technologies. In a case study with measured building loads, the agent (using Claude Haiku 5.5, Sonnet 5.5, and Opus 5.5) achieved an NPC within 1% of the best baseline after far fewer simulations, outperforming random search, TPE Bayesian optimization, and a $(1+1)$ evolution strategy.

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• llms
• agents