基于图形时空特征和S-Stacking组合重建的短期风电场集群功率点间隔预测
Xinxing Hou1, Wenbo Hu2, Maomao Luo1
1GongQing Institute of Science and Technology, Gongqingcheng, 332020, China.
Heliyon
|August 2, 2024
概括
精确的风力发电预测对于风电场集群至关重要. 这项研究引入了一种新的方法,改善了点和间隔预测,提高了电网稳定性和运营效率.
科学领域:
- 可再生能源系统可再生能源系统
- 电力系统运行 电力系统运行
- 计算智能是一种计算智能.
背景情况:
- 风能集成需要精确的预测,以尽量减少运营成本和最大限度地消耗.
- 现有的风力发电预测模型经常忽视风电场集群中的空间相关性和客观的子学习者选择,增加运营风险.
- 风力发电集群的间隔预测开发不足,阻碍了可靠的电网管理.
研究的目的:
- 为风力发电集群开发先进的预测方法,以提高准确性和可靠性.
- 通过结合空间特征和客观的子学习者选择来解决现有模型的局限性.
- 为风电场集群的发电规划提供一个强大的工具.
主要方法:
- 提出了一种新的"分解-聚合-多模型并行预测"方法.
- 数据预处理涉及分解聚合策略和空间特征提取.
- 一个堆叠模型与引导选择的并行子学习者被用于点和间隔预测.
主要成果:
- 与现有模型相比,拟议的方法在点和间隔风力发电预测方面表现出更高的准确性和可靠性.
- 获得了7.47的根平均平方误差 (RMSE) 和1.572.7的平均F值.
- 使用来自中国西北部风电场集群的15分钟分辨率风电数据进行实验验证.
结论:
- 开发的方法为集群环境中的风能预测提供了显著的改进.
- 该方法有效地捕捉空间特征,并优化子学习者选择以提高预测.
- 结果为优化发电规划和风能电网整合提供了可靠的参考.
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