一种基于数据分解的负荷预测方法及系统
By employing a parallel CNN-LSTM prediction method, utilizing variational mode decomposition and the dung beetle algorithm for optimization, and combining CNN and LSTM networks, the problem of insufficient accuracy in load prediction is solved, achieving higher prediction accuracy and reliability.
CN119891152BActive Publication Date: 2026-07-17STATE GRID FUJIAN ELECTRIC POWER CO LTD +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID FUJIAN ELECTRIC POWER CO LTD
- Filing Date
- 2024-11-28
- Publication Date
- 2026-07-17
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Figure CN119891152B_ABST
Abstract
本发明涉及一种基于数据分解的负荷预测方法及系统，其中方法包括以下步骤：获取目标对象的历史负荷数据，并进行相似日选取，获取与目标日期相似的相似日历史负荷数据；利用变分模态分解法从相似日历史负荷数据中分解出多种分量，并利用蜣螂算法对变分模态分解法进行优化；基于分解出的各个分量，分别构建并行的CNN‑LSTM预测模型，并进行训练；通过训练好的CNN‑LSTM预测模型进行目标对象在目标日期的负荷预测。
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