Ele-LLM: A Systematic Evaluation and Adaptation of Large Language Models for Very Short-Term Power Load Forecasting

Power load forecasting is critical for ensuring grid security and stability and optimizing energy resource allocation. The high integration of renewable energy poses significant challenges to traditional methods in data-scarce scenarios. Recently, Large Language Models (LLMs) have shown considerable potential in processing time-series data, yet their effectiveness in very short-term power load forecasting lacks systematic evaluation. This paper proposes a targeted prompt engineering framework and conducts a systematic empirical study on various LLMs, including GPT-4, Claude-3, Gemini, the Llama series, DeepSeek, and Qwen, comparing them with traditional methods such as ARIMA, BiLSTM, MICN, TimesNet, and VMD-BiLSTM. Furthermore, Ele-LLM, a specialized model based on the Low-Rank Adaptation (LoRA) parameter-efficient fine-tuning strategy, is proposed. Experimental results show that Ele-LLM achieves the best forecasting performance (MAPE = 1.04%), significantly outperforming the best traditional baseline. LLMs also demonstrate notable advantages in few-shot learning, long-sequence dependency modeling, and generalization in complex scenarios. This study provides an evaluation benchmark and practical guidelines for applying LLMs in very short-term power load forecasting, proving their great potential and practical value as an emerging technological pathway.