Mid-Term Load Forecasting With Minimal Data: An In-Context-Learning-Aware Approach Using Large Language Models

Abstract

Accurate mid-term load forecasting at the building level is vital for the strategic planning, operation, and sustainability of modern power systems. Machine learning approaches often require large amounts of historical data for training, which may be unavailable in practice. Existing solutions typically rely on transfer learning, which requires extensive data from source domains and task-specific fine-tuning. These approaches are unsuited for mid-term forecasting, where long-range historical data is often limited. To address these challenges, this paper proposes Load-Context, a novel mid-term load forecasting method that leverages the in-context learning (ICL) capability of pre-trained large language models (LLMs). Load-Context consists of a frozen LLM backbone and a lightweight, trainable ICL-aware adapter, enabling effective few-shot and zero-shot forecasting without the need for fine-tuning. We further design a prompt construction strategy that captures spatial correlations among buildings and periodic load patterns. Once pre-trained, the proposed method can generalize to a wide range of target tasks through prompt-based adaptation. Experiments on real-world datasets demonstrate that Load-Context achieves high forecasting accuracy with minimal data.