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一个特定小时的混合DNN-SVR框架,用于全国范围的短期负载预测.
Ervin Čeperić1, Kristijan Lenac2,3
1HEP Telekomunikacije d.o.o., Kumičićeva 13, 51000 Rijeka, Croatia.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了一种新的混合深度神经网络 (DNN) 和支持向量回归 (SVR) 模型,用于准确的短期负载预测 (STLF). 该方法通过整合天气数据和减少预测错误来提高电力系统的效率.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 短期负载预测 (STLF) 对电力系统的稳定性和效率至关重要.
- 准确的预测需要复杂的模型,这些模型可以处理复杂的,非线性模式.
- 整合包括天气预报在内的多种数据源对于提高预报准确性至关重要.
研究的目的:
- 开发和评估混合深度神经网络 (DNN) 和支持向量回归 (SVR) 架构,用于全国范围的,前一天的STLF.
- 提出一个特定小时的框架,包括24个混合模型,以提高预测精度.
- 整合全球数值天气预报数据与本地测量以实现现实的运营设置.
主要方法:
- 开发了一个混合DNN-SVR架构,DNN学习非线性表示,SVR执行最终回归.
- 实施了24个混合型号的小时特定框架.
- 使用梯度增强来选择特征,并将TIGGE天气数据与当地负载和气象数据集成.
主要成果:
- 与独立的DNN,LSTM和变压器基线相比,拟的混合模型在2022年测试年持续减少了预测错误.
- 该框架展示了STLF的可重现的管道.
- 该模型成功地将全球天气预报集成到实际的前一天STLF解决方案中.
结论:
- 混合DNN-SVR框架为现实世界电力系统的STLF精度提供了显著的改进.
- 每小时的具体方法和全球天气数据的整合提高了预报的稳定性和可靠性.
- 本研究提供了一个系统的比较,并为全国范围的前一天的STLF提供了一个实际的解决方案.
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