使用机器学习,深度学习模型和集体集成的增强风能预测
T A Rajaperumal1, C Christopher Columbus2
1School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.
Scientific reports
|July 2, 2025
概括
先进的机器学习和深度学习模型显著提高了风能预测的准确性. 超参数调整组合方法,特别是堆叠组合,增强电网稳定性和可再生能源集成.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 电网稳定与管理 电网稳定与管理
背景情况:
- 风能变化需要准确的预测,以确保电网的稳定性.
- 传统的统计模型在风数据中与复杂的,非线性时间模式作斗争.
- 在利用实时数据,比较模型分析和系统模型选择进行风能预测方面存在差距.
研究的目的:
- 通过先进的机器学习 (ML) 和深度学习 (DL) 技术,提高风能预测性能.
- 通过结合实时数据和系统的超参数调整来解决传统模型的局限性.
- 进行各种ML/DL模型的比较分析,并制定强大的预测策略.
主要方法:
- 评估了各种ML模型 (随机森林,XGBoost等) 和DL模型 (LSTM,MLP).
- 利用SCADA和实时风数据,结合风速等天气特征.
- 从表现最好的单个模型开发了一个堆叠组合模型,以提高可靠性.
主要成果:
- 随机森林 (RF) 在初始数据集中表现出色;RF,XGBoost和堆叠集团在实时数据中表现出色.
- 堆叠组合实现了高达0.998的R平方值,低MAE,MSE和RMSE.
- 超参数调整和合体方法显著提高了预测准确性和预测可靠性.
结论:
- 先进的ML/DL技术,特别是超参数调节的堆叠组合,对于风能预测非常有效.
- 改进的预测支持更好的资源管理,电网可靠性和可再生能源的运营规划.
- 该研究促进了可再生能源预测,为全球可持续性目标做出了贡献.
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