← 研踪 PaperTrailarXiv:2610.35160 · v2
AutoEnergyDesign: An LLM-Driven Agent for Automatic Energy System Design Optimization
首次提交 2026-10-26 18:25
版本更新 2026-11-04 23:58
雷达观测 2026-11-05 12:13
摘要
The design optimization of building energy systems represents a complex, high-dimensional search problem involving discrete technology selections, continuous sizing parameters, and intricate economic trade-offs in which every candidate must be evaluated by a year-long simulation. This paper presents AutoEnergyDesign, in which a Large Language Model (LLM) agent reasons over past simulation results, proposes configuration changes and learns in context from each outcome, while a git-based mechanism reverts changes that do not improve the design. The agent couples a reasoning model with explicit memories, a constrained action interface, and an environment-grounded keep/revert feedback signal. The simulator covers eight technologies and computes the 25-year Net Present Cost (NPC). On a case study with measured building loads, the agent, instantiated with three Claude models (Haiku 5.5, Sonnet 5.5 and Opus 5.5; ten runs of 50 simulations each), is compared with random search, Tree-structured Parzen Estimator (TPE) Bayesian optimization and a (1+1) evolution strategy that shares the agent's acceptance rule (ten seeds of 300 evaluations). With all three models the agent reaches an NPC of 3.53MEUR, 1.20% and 0.23% below the best results of TPE and of the evolution strategy, and comes within 1% of it after a median of 18 to 27.5 simulations; among the baselines, only the evolution strategy does so, after a median of 192 evaluations. Every decision is recorded, making the search auditable.
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v2 · 2026-11-04 23:58 · 最新观测版本
AutoEnergyDesign: An LLM-Driven Agent for Automatic Energy System Design Optimization
The design optimization of building energy systems represents a complex, high-dimensional search problem involving discrete technology selections, continuous sizing parameters, and intricate economic trade-offs in which every candidate must be evaluated by a year-long simulation. This paper presents AutoEnergyDesign, in which a Large Language Model (LLM) agent reasons over past simulation results, proposes configuration changes and learns in context from each outcome, while a git-based mechanism reverts changes that do not improve the design. The agent couples a reasoning model with explicit memories, a constrained action interface, and an environment-grounded keep/revert feedback signal. The simulator covers eight technologies and computes the 25-year Net Present Cost (NPC). On a case study with measured building loads, the agent, instantiated with three Claude models (Haiku 5.5, Sonnet 5.5 and Opus 5.5; ten runs of 50 simulations each), is compared with random search, Tree-structured Parzen Estimator (TPE) Bayesian optimization and a (1+1) evolution strategy that shares the agent's acceptance rule (ten seeds of 300 evaluations). With all three models the agent reaches an NPC of 3.53MEUR, 1.20% and 0.23% below the best results of TPE and of the evolution strategy, and comes within 1% of it after a median of 18 to 27.5 simulations; among the baselines, only the evolution strategy does so, after a median of 192 evaluations. Every decision is recorded, making the search auditable.
查看此版本解读 →查看此版本原文 ↗v1 · 2026-10-26 18:25
AutoEnergyDesign: An LLM-Driven Agent for Automatic Energy System Design Optimization
Designing building energy systems is a high-dimensional, mixed-integer search problem: when each design is evaluated by a year-long simulation, black-box optimizers typically need hundreds of evaluations. This paper presents AutoEnergyDesign, a framework in which a Large Language Model (LLM) agent reasons over previous simulation results, proposes configuration changes and learns in context from each outcome, while a git-based mechanism automatically reverts unsuccessful changes. The agent couples a reasoning model with an explicit memory organisation (procedural, semantic, episodic and reflective), a constrained action interface and an environment-grounded keep/revert signal. The simulator covers eight technologies (photovoltaics, micro-wind, combined heat and power, solar thermal, gas boilers, batteries, heat pumps and inverters) and computes the 25-year Net Present Cost (NPC), including capital and operating costs and salvage value. On a case study with measured building load profiles, the agent, run with Claude Haiku 5.5, Sonnet 5.5 and Opus 5.5 (ten runs of 50 simulations each), is compared with random search, Tree-structured Parzen Estimator (TPE) Bayesian optimization and a $(1+1)$ evolution strategy sharing the agent's acceptance rule (300 evaluations, ten seeds). With all three models the agent reaches an NPC of about 3.53 MEUR, 1.20% below the best TPE result and 0.23% below the best evolution-strategy result. It comes within 1% of this design after a median of 18 (Opus) to 27.5 (Haiku) simulations, whereas among the baselines only the evolution strategy does so, in half of its seeds and after a median of 192 evaluations. Opus reaches the best design in all ten runs, Sonnet in six (two runs never come within 1%), and Haiku comes within 1% in every run but reaches it exactly only once. Every decision is recorded in an auditable trace.
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