Utilizing Language Models for Energy Load Forecasting
Hao Xue Flora D. Salim
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.