具有卷积特征提取的深度残余网络用于短期负载预测.
Junchen Liu1, Faisul Arif Ahmad2, Khairulmizam Samsudin1
1Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), 43400, Serdang, Selangor, Malaysia.
Scientific reports
|January 26, 2026
概括
这项研究引入了一种新的CNN嵌入深度残留网络 (DRN),用于准确的短期负载预测 (STLF). 该模型增强了特征提取和概括,在各种气候中表现优于现有的方法.
科学领域:
- 人工智能的人工智能
- 电气工程 电气工程
- 数据科学数据科学数据科学
背景情况:
- 深度学习模型在平衡特征提取和短期负载预测 (STLF) 的时间表示方面面临着挑战.
- 现有的方法往往缺乏跨不同气候条件的概括性,影响预测准确性.
研究的目的:
- 开发一个卷积神经网络嵌入深度残余网络 (CNN-嵌入DRN) 改进STLF.
- 在DRN框架内使用CNN来增强本地特征提取和时间模式识别.
- 评估模型在不同气候区的概括性和稳定性.
主要方法:
- 将卷积神经网络 (CNN) 集成到深度残余网络 (DRN) 中,用于局部特征提取.
- 应用剩余学习来提高网络稳定性和减轻梯度退化.
- 对温带 (ISO-NE) 和热带 (马来西亚) 数据集的基线和废弃模型进行比较性性能评估.
- 使用引导分析验证统计学意义和季节性稳定性.
主要成果:
- 嵌入CNN的DRN实现了最低的平均绝对百分比错误 (MAPE),在ISO-NE数据集上为1.5303%,在马来西亚数据集上为5.0566%.
- 该模型与所有基线和废弃模型相比,显示出优异的预测性能.
- 引导式分析证实了性能改进的统计学意义.
结论:
- 拟议的CNN嵌入式DRN为STLF提供了一个可靠和可通用的框架.
- 该模型表现出提高的准确性,稳定性和适应不同气候和需求条件的适应性.
- 未来的工作包括扩大多区域预测的框架,并纳入注意力机制.
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