基于深度学习的混合模型用于短期负载预测和智能电网信息管理
Xinyu Wen1, Jiacheng Liao2, Qingyi Niu3
1School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, 250014, China.
Scientific reports
|June 14, 2024
概括
本研究介绍了一种混合深度学习模型,用于精确预测智能电网中的短期电力负载. 该方法提高了预测准确性和效率,改善了电网运行.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 准确的电力负载预测对于智能电网的可持续性至关重要.
- 挑战包括复杂的负载动态,不确定性和高维能量数据.
- 现有的方法与复杂的特征和长期依赖性作斗争.
研究的目的:
- 为准确的短期功率负载预测开发一种计算方法.
- 改进智能电网系统中的能源信息管理.
- 为了提高未来负载需求的预测.
主要方法:
- 一个混合深度学习模型,结合了Gated Recurrent Unit (GRU) 和时间卷积网络 (TCN).
- 整合注意力机制,专注于相关的输入特征.
- 对公共数据集的评估,包括GEFCom2014.
主要成果:
- 拟议的算法在预测准确性,效率和稳定性方面优于基线模型.
- 关于GEFCom2014:FLOP减少了48.8%,推断时间减少了46.7%,MAPE提高了39%.
- 在智能电网可靠性,稳定性和成本效益方面取得了显著的改进.
结论:
- 带有注意力的混合GRU-TCN模型有效地解决了功率负载预测方面的挑战.
- 该方法改善了智能电网的运营规划和风险评估.
- 提高智能电网系统的整体性能和经济可行性.
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