| 标题 |
PowerGPT:FOUNDATION MODEL FOR POWER SYSTEMS
PowerGPT：电力系统基础模型
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| 其它 | Abstract We propose a foundation model, namely PowerGPT , to model electricity time series (ETS) data, which learns generic representations of load and electricity consumption data by pre-training, providing a large-scale, off-the-shelf model for power systems. PowerGPT is the largest model in the field of power systems and is pre-trained on a large-scale ETS data including load and electricity consumption data. The design of PowerGPT is to capture long-term temporal dependency and hierarchical correlation from massive ETS data, providing information that spans from the fine-grained to coarse-grained scales. As a foundation model, PowerGPT achieves SOTA performance on various downstream tasks in power systems (i.e. forecasting, missing value imputation, and anomaly detection), showing the generalization ability to a wide range of tasks. The low-resource label analysis further illustrates the effectiveness of our pre-training strategy. |
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