用大模型融合文本与数据，提升风电超短期预测精度。
M2WLLM: Multi-Modal Multi-Task Ultra-Short-term Wind Power Prediction Algorithm Based on Large Language Model
- 将文本提示与数值时间序列融合，通过提示嵌入和语义增强实现多模态输入。
- 在三省风电数据上优于GPT4TS，多时距预测均更准确，少样本学习能力强。
- 适合需要高精度、小样本风电预测的能源调度与电网管理场景。
风能并网需精准的超短期风电功率预测以保障电网稳定与资源优化配置。本文提出M2WLLM，一种基于大语言模型（LLM）的多模态多任务超短期风电预测算法。该模型通过整合文本信息与时间序列数值数据，突破传统方法与深度学习的局限。其架构包含提示嵌入器与数据嵌入器，其中数据嵌入器中的语义增强模块将时间数据转化为大模型可理解的格式，有效提取隐含特征。在三个中国省份风电场数据上的实证评估显示，M2WLLM在多种数据集与预测时距下持续优于GPT4TS等现有方法，验证了大模型在超短期预测中提升准确率与鲁棒性的潜力，并展现出强大的少样本学习能力。
原文摘要 · Abstract (English)
The integration of wind energy into power grids necessitates accurate ultra-short-term wind power forecasting to ensure grid stability and optimize resource allocation. This study introduces M2WLLM, an innovative model that leverages the capabilities of Large Language Models (LLMs) for predicting wind power output at granular time intervals. M2WLLM overcomes the limitations of traditional and deep learning methods by seamlessly integrating textual information and temporal numerical data, significantly improving wind power forecasting accuracy through multi-modal data. Its architecture features a Prompt Embedder and a Data Embedder, enabling an effective fusion of textual prompts and numerical inputs within the LLMs framework. The Semantic Augmenter within the Data Embedder translates temporal data into a format that the LLMs can comprehend, enabling it to extract latent features and improve prediction accuracy. The empirical evaluations conducted on wind farm data from three Chinese provinces demonstrate that M2WLLM consistently outperforms existing methods, such as GPT4TS, across various datasets and prediction horizons. The results highlight LLMs' ability to enhance accuracy and robustness in ultra-short-term forecasting and showcase their strong few-shot learning capabilities.
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