Empower Pre-Trained Large Language Models for Building-Level Load Forecasting
Yating Zhou, Meng Wang
Abstract
Short-term building-level load forecasting is significant for enhancing the stability and efficiency of power grids. Despite the superior forecasting performance, machine learning methods heavily rely on sufficient historical load data for training. This paper addresses the challenge of limited or unavailable historical data, which often occurs in new communities or due to data storage issues. This paper proposes BlackInter, a novel black-box tuning inductive adapter based on pre-trained large language models (LLMs), specifically tailored for building-level load forecasting. Our method leverages the inherent generalization capabilities of LLMs with no need for pre-training on similar domains and fine-tuning by target data. Furthermore, BlackInter exploits spatial correlations among nearby buildings to improve forecast accuracy. It directly adapts to different building sizes, a feature lacking in existing approaches. Our approach does not require prior knowledge of the LLM's structure or parameters. We validate the effectiveness of the LLM-based BlackInter using real-world datasets and compare it with four existing methods. Under the settings of limited and no historical data, the prediction error by our method can be only 42.88% and 69.44% of the error by the best alternative methods, respectively.