基于多变量信息融合和深度学习网络的变站设备温度预测
Lijie Sun1, Chunxue Liu2, Ying Wang3
1School of Electronics and Information Engineering, Taizhou University, Taizhou, Zhejiang, China.
PeerJ. Computer science
|June 22, 2023
概括
准确的变电站设备温度预测是具有挑战性的. 本研究提出了一种使用多变量信息融合,卷积神经网络 (CNN) 和封闭循环单元 (GRU) 的新方法,以提高预测准确度.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 由于季节性,不稳定性和有限的数据,变电站设备温度预测是复杂的.
- 传统的方法与热数据的动态和多变量性质作斗争.
研究的目的:
- 开发一种准确和可靠的方法来预测变电站设备温度.
- 利用多变量信息融合和深度学习来进行增强的热分析.
主要方法:
- 使用相关性分析的特征工程创建一个多变量信息融合特征向量 (MIFFV).
- 使用主要组件分析 (PCA) 减少MIFFV的维度,以获得减少的特征向量 (RFV).
- 深度学习模型,包括用于特征提取的卷积神经网络 (CNN) 和用于时间序列预测的门循环单位 (GRU).
主要成果:
- 结合环境,时间和空间特征的MIFFV显著提高了对单个或双重特征向量的预测性能.
- 拟议的CNN-GRU模型使用RFV作为输入,与其他四种模型相比,显示出更高的预测准确性.
- 评估指标包括平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE).
结论:
- 拟议的多变量信息融合和深度学习方法有效地解决了变电站设备温度预测方面的挑战.
- CNN和GRU的整合为分析复杂的热力学提供了一个强大的框架.
- 这种方法为提高电力基础设施的可靠性和维护提供了一个有希望的解决方案.
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