中国的短期电力负载预测:基于VDD-SE的Bi-SATCN神经网络模型
Yuan Huang1, Qimeng Feng1, Feilong Han1
1School of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China.
PloS one
|September 30, 2024
概括
本研究引入了一种新的VMD-SE-BiSATCN模型,用于准确的短期电力负载预测,这对于中国双碳政策下的可再生能源整合至关重要. 该模型提高了预测准确性和效率,解决了系统的复杂性.
科学领域:
- 电气工程 电气工程
- 能源系统分析 能源系统分析
- 计算智能是一种计算智能.
背景情况:
- 准确的短期电力负载预测对于电力系统的稳定性和规划至关重要.
- 中国的"双碳"政策需要整合可再生能源,在电网中引入非线性和不稳定性.
- 现有的预测方法难以应对可再生能源带来的复杂性.
研究的目的:
- 提出一个先进的预测模型VMD-SE-BiSATCN,以提高短期功率负载预测的准确性.
- 解决因可再生能源整合而导致的电力系统非线性和不稳定的挑战.
- 提高时间序列预测中捕捉长期依赖性的能力.
主要方法:
- 变量模式分解 (VMD) 用于序列组件分析.
- 使用样本 (SE) 来分析和重建序列组件的复杂性.
- 一个自我注意力机制被整合到一个时间卷积网络 (TCN) 中,以改善长期依赖性捕获.
- 拟议的VMD-SE-BiSATCN模型使用竞争数据和现实世界功率负载数据进行了评估.
主要成果:
- 与其他基准模型相比,VDD-SE-BiSATCN模型表现出优异的性能.
- 实现了 92.87.7 的平均绝对误差 (MAE).
- 实现了根平均平方误差 (RMSE) 的 126.906.6.
- 实现了0.81%的平均绝对百分比误差 (MAPE).
结论:
- VMD-SE-BiSATCN模型显著提高了短期功率负载预测的准确性和效率.
- 该模型有效地处理与可再生能源整合相关的复杂性.
- 这种方法在不断变化的能源政策下为电力系统规划和运营提供了强大的解决方案.
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