Energy Data Forecasting Based on the STRLM Time Series Prediction Model

We propose an improved model, the Seasonal-Trend Relational Large Language Model (STRLM), for time-series prediction, which enhances prediction accuracy in energy-related applications and achieves robust performance under data-scarce conditions. The key methodological contributions include: (1) employing Exponential Moving Average (EMA) to decompose preprocessed time-series data into trend and seasonal components, facilitating the learning of complex temporal patterns; (2) introducing a dual-scale Graph Neural Network (GNN) to ensure structural and logical alignment between sequential and relational data for effective LLM adaptation; and (3) optimizing LLM performance through few-shot prompt-based context alignment to improve adaptability to limited training data. Extensive evaluations across multiple datasets demonstrate that STRLM consistently outperforms state-of-the-art baselines, particularly in few-shot and zero-shot scenarios, achieving superior performance in both MSE and MAE metrics across prediction horizons (96 to 720 steps) with a consistent improvement margin of $\mathbf{4 \% - 1 3 \%}$ over competing models.