通过深度时空图表生成稀疏编码与囊网络进行测量背后负荷和光伏分类
概括
这项研究引入了一种新型的时空图稀疏编码囊网络,用于精确的测量表 (BtM) 背后负荷和光伏 (PV) 生产估计. 该方法显著提高了住宅能源分类的准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 可持续能源 可持续能源
背景情况:
- 屋顶光伏 (PV) 电池板对于可持续能源至关重要,但它们的整合影响了配电网.
- 精确估计后台计 (BtM) 负载和光伏功率对于电网运行至关重要,因为客户负载配置的变化.
研究的目的:
- 开发一种先进的方法,用于在住宅区准确估计BtM负载和光伏发电量.
- 解决分布式光伏系统在配电网络中的集成所带来的挑战.
主要方法:
- 提出了一个时空图稀疏编码 (SC) 囊网络,将SC与深度生成图模型和囊网络集成在一起.
- 模型邻近的住宅单元作为一个动态图,使用光谱图卷积 (SGC) 注意力视孔长短期记忆 (PLSTM) 来提取模式.
- 在编码器解码器的隐藏层中学习稀疏表示的字典,以增强隐藏空间稀疏性.
主要成果:
- 在Pecan Street和Ausgrid数据集上,分别在BtM PV和负载估计中实现了超过9.8%和6.3%的根平均平方误差 (RMSE) 改进.
- 与现有的最先进的方法相比,在能源分类任务中表现出优异的性能.
结论:
- 拟议的SC囊网络有效地估计了BtM光伏发电和住宅负载.
- 这种方法为高光伏透率的配电系统的运营管理提供了显著的进步.
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