Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application

TL;DR

Helios is a specialized large language model designed for smart energy applications, built with domain-specific datasets and resources to improve knowledge, task performance, and industry alignment.

Contribution

The paper introduces Helios, a domain-specific LLM for smart energy, along with datasets and benchmarks to enhance its knowledge, task accuracy, and industry relevance.

Findings

Helios outperforms baseline models in smart energy tasks.

The datasets improve domain knowledge and task performance.

Helios demonstrates better alignment with human preferences.

Abstract

In the global drive toward carbon neutrality, deeply coordinated smart energy systems underpin industrial transformation. However, the interdisciplinary, fragmented, and fast-evolving expertise in this domain prevents general-purpose LLMs, which lack domain knowledge and physical-constraint awareness, from delivering precise engineering-aligned inference and generation. To address these challenges, we introduce Helios, a large language model tailored to the smart energy domain, together with a comprehensive suite of resources to advance LLM research in this field. Specifically, we develop Enersys, a multi-agent collaborative framework for end-to-end dataset construction, through which we produce: (1) a smart energy knowledge base, EnerBase, to enrich the model's foundational expertise; (2) an instruction fine-tuning dataset, EnerInstruct, to strengthen performance on domain-specific downstream tasks; and (3) an RLHF dataset, EnerReinforce, to align the model with human preferences and industry standards. Leveraging these resources, Helios undergoes large-scale pretraining, SFT, and RLHF. We also release EnerBench, a benchmark for evaluating LLMs in smart energy scenarios, and demonstrate that our approach significantly enhances domain knowledge mastery, task execution accuracy, and alignment with human preferences.