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Energy load forecasting plays a crucial role in optimizing resourceallocation and managing energy consumption in buildings and cities. In thispaper, we propose a novel approach that leverages language models for energyload forecasting. We employ prompting techniques to convert energy consumptiondata into descriptive sentences, enabling fine-tuning of language models. Byadopting an autoregressive generating approach, our proposed method enablespredictions of various horizons of future energy load consumption. Throughextensive experiments on real-world datasets, we demonstrate the effectivenessand accuracy of our proposed method. Our results indicate that utilizinglanguage models for energy load forecasting holds promise for enhancing energyefficiency and facilitating intelligent decision-making in energy systems.

Utilizing Language Models for Energy Load Forecasting