A Large-Scale Language Model Based System for Automated Generation of Offshore Wind Power Feasibility Study Reports

Abstract

Driven by global energy transition goals, the large-scale development of offshore wind power imposes rigid requirements for professionalism, standardization, and timeliness on feasibility study reports (FSR). Traditional manual compilation and existing automated methods fail to meet these requirements due to interdisciplinary complexity, poor process controllability, and insufficient domain adaptation. To address these challenges, this paper proposes a configurable and interpretable offshore wind FSR generation system built on a three-tier framework that encompasses “data support, process orchestration, and quality assurance”. The system integrates a YAML-based workflow architecture, multi-level prompt engineering, and a comprehensive evaluation system. Notably, the introduced “Cyclic Aggregation Mode” enables the iterative generation and logical summarization of multi-subproject data, effectively distinguishing this system from traditional linear text generation models. Experimental results demonstrate that the proposed “Retrieval-Augmented Generation (RAG) + Large-scale Language Model (LLM) + Workflow” system outperforms baseline models with key metrics including semantic consistency (0.6592), information coverage (0.3908), structural compliance (0.5123), and an overall score (0.5965). Ablation studies validate the independent contributions of the RAG and Workflow components, thereby establishing the “RAG + LLM + Workflow” paradigm for intelligent professional document generation. This work addresses core challenges related to controllability, accuracy, and interpretability in high-stakes decision-making scenarios while providing a reusable technical pathway for the automated feasibility demonstration of offshore wind power projects.

Keywords

Offshore wind power / Feasibility study report generation / Large language models / Retrieval-augmented generation / Workflow / Prompt engineering

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

In the preparation of this work, the authors utilized DeepSeek to assist with grammatical corrections of the manuscript. Following the use of this tool, the authors reviewed and edited the content as necessary and assume full responsibility for the publication’s content.

Acknowledgements

The authors would like to thank the financial support of Nanning city science and Technology Bureau.

Author Contributions

Methodology, M.L.; Software, T.L.; Validation, Y.Y.; Investigation T.L., X.M. and Z.J.; Writing Original Draft Preparation, T.L.; Review & Editing, J.Z. and Y.W.; Supervision, Q.M.; Project Administration, M.L.; Funding Acquisition, M.L.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available on request.

Funding

This research was funded by the Key R&D Program of the Nanning Science Research and Technology Development Plan grant number (20253057).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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