相关实验视频
以排名为导向的机器学习框架,用于具有时间可靠性约束的概率风电预测
Chaojie Li1, Jiang Dai2, Shijin Tian2
1Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang, 550002, China.
Scientific reports
|November 26, 2025
概括
这项研究引入了一种新的风力发电预测框架,以确保网络稳定性的时间一致性和排名准确性. 新模型显著提高了预测性能,特别是在波动的风条件下.
科学领域:
- 可再生能源系统可再生能源系统
- 机器学习用于能源.
- 时间序列预测时间序列预测
背景情况:
- 准确的风力发电预测对于电网稳定性和能源市场效率至关重要.
- 传统的方法往往忽视了输出排序和时间一致性,影响了基于排名的关键决策.
- 现有的模型在与风力发电的动态性质作斗争,特别是在高波动状态下.
研究的目的:
- 开发一个新的风力发电预测框架,集成排名一致性和时间平滑性.
- 为了解决传统方法在处理有序输出的局限性,用于电网管理任务.
- 提高风力发电预测的准确性和可靠性,特别是在不同的风力条件下.
主要方法:
- 开发了一个深度的神经架构,利用注意力机制进行端到端的训练.
- 引入了一个复合的多目标损失函数,以最大限度地减少预测错误,最大限度地提高排名对齐,并强制执行时间排名规范化.
- 构建了一个高分辨率的数据集与同步的SCADA,气象和地理数据,包括标记的风势.
主要成果:
- 拟议的模型在MAE,RMSE和NDCG中表现优于基线方法 (LSTM,变压器,LambdaMART).
- 在预测准确度方面取得了显著的改进,特别是在低,斜坡和和风状态下.
- 与最先进的替代品相比,在时间排名稳定指数 (TRSI) 中表现出高达35%的改善.
结论:
- 新的多目标损失函数使得排名意识和时间稳定的风力预报成为可能.
- 新的风力模式标记数据集有助于全面评估预测和排名能力.
- 这些发现为将等级敏感智能集成到实际的网格规模预测管道中铺平了道路.
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