Multi-task Short-term Power Load Forecasting With Multi-seasonal Time-slot Tiered Bi-directional Cluster and Temporal Transfer Learning

In the field of power systems, the temporal and seasonal variations in electricity demand caused intermittent distribution shifts in the time-series data of grid load. The above phenomenon makes it difficult for the general load forecasting model to effectively mine and utilize the information of the dynamic change data, which reduces the accuracy of the power load forecasting model. To address these problems, this paper proposes a multi-task, short-term power load forecasting model that integrates cross-quarter multi- period bidirectional clustering with temporal transfer learning. The method adopts a hierarchical processing approach: first, it identifies periods with significant load distribution differences through clustering analysis. Then it applies multi-task learning to model the sequence predictions within each period, facilitating information sharing and enhancing prediction performance. Subsequently, temporal transfer learning is used to adapt to the distribution differences within each sub-task, further mitigating the impact of these differences on the modeling process. The experimental results show that compared with the existing mainstream forecasting methods, the proposed method shows better forecasting performance in the real power load forecasting, especially when the data distribution changes significantly, the prediction error is significantly reduced, and it provides more reliable support for power grid scheduling and energy management.