基于集群和动态识别的自动储存神经网络:用于短期公园电力负载预测的等待方法
Jingyao Liu1, Jiajia Chen1, Guijin Yan1
1School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China.
iScience
|August 14, 2023
概括
一种名为聚类和动态识别的自动储存神经网络 (CDbARNN) 的新方法改善了工业微电网的短期负载预测. 这种方法通过动态识别和集群负载模式来提高预测准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 能源系统 能源系统
背景情况:
- 准确的短期负载预测 (STLF) 对工业园区微电网的稳定运行至关重要.
- 传统的预测方法经常与微电网固有的复杂和动态负载模式作斗争.
研究的目的:
- 提出一种基于集群和动态识别的自动储存神经网络 (CDbARNN),用于工业园区微电网中增强STLF.
- 通过利用动态模式识别和集群技术,提高负载预测的准确性和效率.
主要方法:
- 使用K-means集群将负载数据分解成不同的集群.
- 动态识别技术根据特征信息识别输入负载系列的集群成员.
- 输入负载序列和相应的集群数据形成了自动储存神经网络的高维矩阵.
- 库节点数的优化是为了匹配不同的集群而进行的.
主要成果:
- 拟议的CDbARNN在STLF中展示了实际工业园区微电网的优越性能.
- 数字实验显示,与其他成熟的深度学习方法相比,显著改善.
- 该方法通过动态识别和集群有效处理复杂的负载变化.
结论:
- CDbARNN为工业园区微电网中的STLF提供了强大而准确的解决方案.
- 集群和动态识别的整合显著提高了预测能力.
- 这种方法为智能电网能源管理提供了有前途的进步.
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