Power load prediction method, device, equipment and medium
By employing a cross-modal alignment and large language model-based power load forecasting method, the problem of insufficient forecast stability in heterogeneous scenarios with multi-source data is solved, achieving high-precision, robust, and interpretable power load forecasting, which is applicable to short-term and medium-to-long-term load forecasting of power systems.
Patent Information
- Application Number
- CN202610047377.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-14
- Publication Date
- 2026-04-17
AI Technical Summary
Existing technologies for power load forecasting suffer from insufficient forecast stability and weak interpretability in heterogeneous scenarios with multi-source data. Furthermore, large language models are prone to losing accurate numerical features and dynamic evolution patterns of the system during the time series to text conversion process, resulting in insufficient forecast accuracy and robustness.
A power load prediction method based on cross-modal alignment and a large language model is adopted. By constructing a dual-modal coding architecture and a cross-modal similarity alignment mechanism, historical power load data is input into the time series coding branch and the large language model. Time series embeddings and semantic embeddings are extracted respectively, and feature alignment between modes is achieved through a channel similarity retrieval mechanism. Finally, the power load prediction result is output.
It improves the accuracy, robustness, and interpretability of power load forecasting, and is applicable to short-term and medium-to-long-term load forecasting scenarios in power systems. It also reduces computational costs and enhances the model's generalization performance.