Opportunities and Challenges of Applying Large Language Models in Building Energy Efficiency and Decarbonization Studies: An Exploratory Overview
원문 초록
In recent years, the rapid advancement and impressive capabilities of Large Language Models (LLMs) have been evident across various domains. This paper explores the application, implications, and potential of LLMs in building energy efficiency and decarbonization studies. The wide-ranging capabilities of LLMs are examined in the context of the building energy field, including intelligent control systems, code generation, data infrastructure, knowledge extraction, and education. Despite the promising potential of LLMs, challenges including complex and expensive computation, data privacy, security and copyright, complexity in fine-tuned LLMs, and self-consistency are discussed. The paper concludes with a call for future research focused on the enhancement of LLMs for domain-specific tasks, multi-modal LLMs, and collaborative research between AI and energy experts.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs
Kodezi Chronos: A Debugging-First Language Model for Repository-Scale, Memory-Driven Code Understanding
Rethinking Verification for LLM Code Generation: From Generation to Testing
EvoAgentX: An Automated Framework for Evolving Agentic Workflows
CoreCodeBench: A Configurable Multi-Scenario Repository-Level Benchmark
Estimating Correctness Without Oracles in LLM-Based Code Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.