M2WLLM: Multi-Modal Multi-Task Ultra-Short-term Wind Power Prediction Algorithm Based on Large Language Model

TL;DR

This paper presents M2WLLM, a novel multi-modal large language model-based approach for ultra-short-term wind power prediction, integrating textual and numerical data to significantly improve forecasting accuracy and robustness.

Contribution

It introduces a multi-modal LLM architecture with a Prompt Embedder, Data Embedder, and Semantic Augmenter, enabling effective fusion of textual and numerical data for wind power forecasting.

Findings

M2WLLM outperforms existing methods like GPT4TS across multiple datasets.

The model demonstrates strong few-shot learning capabilities.

Empirical results show improved accuracy and robustness in wind power prediction.

Abstract

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.