基于封闭的循环单元神经网络和修改后的未来搜索算法,预测智能电网中的电力消耗和水电生产情况
Hao Tang1, Yuening Wang2, Xinping Yuan3
1Information Center, Yunnan Power Grid Co., LTD, Kunming, 650000, Yunnan, China. tangtonight@163.com.
Scientific reports
|December 22, 2025
概括
精确的电力使用和水力发电预测是通过使用一种新的Gated Recurrent Unit (GRU) 神经网络和修改后的未来搜索算法 (MFSA) 模型实现的. 这种优化的方法提高了智能电网效率和资源分配.
科学领域:
- 能源系统工程 能源系统工程
- 网格中的人工智能
- 预测可再生能源的使用情况
背景情况:
- 高效的能源分配和减少浪费对于电网性能至关重要.
- 准确预测电力使用和水电生产对于资源管理至关重要.
- 先进的机器学习模型可以提高智能电网中的预测准确性.
研究的目的:
- 开发一种优化的模型,用于预测智能电网中的电力使用和水电发电.
- 整合经济,社会和气候数据,以提高预测准确度.
- 评估与现有方法对比开发模型的性能.
主要方法:
- 运用了Gated Recurrent Unit (GRU) 神经网络和修改后的未来搜索算法 (MFSA).
- 用经济和社会数据来预测电力使用情况.
- 集成的气候数据用于水电发电模拟和预测.
主要成果:
- 开发的GRU-MFSA模型在预测方面表现出卓越的准确性.
- 优化模型的表现优于原始的深度学习和其他优化方法.
- 实现了对电力使用和水电发电的准确预测.
结论:
- 优化的GRU-MFSA模型对于智能电网能源预测非常有效.
- 该模型增强了电力系统的决策和资源分配.
- 这种方法为管理电力需求和可再生能源供应提供了显著的改进.
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