Energy load forecasting plays a crucial role in optimizing resource allocation and managing energy consumption in buildings and cities. In this paper, we propose a novel approach that leverages language models for energy load forecasting. We employ prompting techniques to convert energy consumption data into descriptive sentences, enabling fine-tuning of language models. By adopting an autoregressive generating approach, our proposed method enables predictions of various horizons of future energy load consumption. Through extensive experiments on real-world datasets, we demonstrate the effectiveness and accuracy of our proposed method. Our results indicate that utilizing language models for energy load forecasting holds promise for enhancing energy efficiency and facilitating intelligent decision-making in energy systems.

翻译：能源负荷预测在优化建筑和城市的资源分配及管理能源消耗方面起着关键作用。本文提出了一种新颖的方法，利用语言模型进行能源负荷预测。我们采用提示技术将能源消耗数据转化为描述性句子，从而实现对语言模型的微调。通过采用自回归生成方法，我们提出的方法能够预测不同时间范围的未来能源负荷消耗。基于真实数据集的广泛实验表明，我们所提方法具有有效性和准确性。研究结果表明，利用语言模型进行能源负荷预测有望提升能源效率，并促进能源系统智能决策的实现。