基于模糊集群的深度学习用于电网系统中使用时间变化和时间不变特征的短期负载预测
Kit Yan Chan1, Ka Fai Cedric Yiu2, Dowon Kim1
1School of Electrical Engineering, Computing and Mathematics Sciences, Curtin University, Bentley, WA 6102, Australia.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了一种基于模糊集群的新型深度神经网络 (DNN) 用于短期负载预测 (STLF). 新模型整合了用户特定的时间不变特征,大大提高了对现有方法的预测准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的短期负载预测 (STLF) 对电网可靠性和效率至关重要.
- 深度神经网络 (DNN) 对STLF具有前景,因为它们能够建模复杂的时间序列数据.
- 现有的STLF DNN主要利用时间变化的特征,忽视了有价值的时间不变的用户特征.
研究的目的:
- 为增强的STLF提出一种基于模糊集群的新型DNN.
- 整合时间变化和时间不变的用户功能,以提高预测准确度.
- 通过利用模糊集群开发一个更简单,更有效的DNN模型.
主要方法:
- 使用模糊集群算法,根据类似的时间不变特征 (例如建筑特征) 将用户分组.
- 随后,为每个集群开发深度神经网络 (DNN) 模型,重点关注时间变化的特征.
- 拟议的模型将模糊集群与DNN结合起来,使用两种特征类型执行STLF.
主要成果:
- 基于模糊集群的DNN在STLF中表现出高于标准DNN的性能.
- 通过模糊集群集成时间不变特征的集成导致了更准确的负载预测.
- 提出的方法表现优于常用的模型,如长期短期记忆 (LSTM) 和卷积神经网络 (CNN).
结论:
- 整合时间不变的用户功能显著提高了STLF的准确性.
- 模糊集群提供了一个有效的机制来整合这些特征,简化DNN模型.
- 拟议的方法为电力系统的短期负载预测提供了更有效和更准确的解决方案.
相关概念视频
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