一种基于T-LSTNet_Markov的组合预测方法,用于短期风力发电预测
Yongsheng Wang1,2, Yuhao Wu1, Hao Xu1,2
1College of Data Science and Application, Inner Mongolia University of Technology, Hohhot, China.
Network (Bristol, England)
|May 29, 2023
概括
这项研究引入了一种新的组合模型,用于准确的短期风力发电预测. 通过将T-LSTNet与马尔科夫过程集成,该模型显著提高了可再生能源电网的预测可靠性.
科学领域:
- 可再生能源系统可再生能源系统
- 计算智能是一种计算智能.
- 时间序列预测时间序列预测
背景情况:
- 风力发电对于清洁能源至关重要,但由于发电波动而面临挑战.
- 准确的短期风能预测对于稳定的电网整合至关重要.
- 现有的预测模型与风能固有的不确定性作斗争.
研究的目的:
- 开发和验证一个混合模型,以改善短期风力发电预测.
- 为应对风力发电间歇性在电网连接系统中所带来的挑战.
- 用先进的计算技术提高风能预测的准确性.
主要方法:
- 数据预处理和清理原始风力发电数据.
- 在预处理数据上使用T-LSTNet模型进行预测.
- 使用k-means++集群和加权马尔科夫过程进行错误纠正.
主要成果:
- 该T-LSTNet模型提供了初步的风力发电预测.
- 错误分析显示,预测值与实际值之间存在差异.
- 结合的T-LSTNet_markov模型在经过错误纠正后显示出更高的预测准确性.
结论:
- 拟议的组合模型有效地提高了短期风力发电预测的准确性.
- 纠错技术对于提高风能预测可靠性至关重要.
- T-LSTNet_markov方法为连接到电网的风力发电系统提供了一个有前途的解决方案.
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