从领先的指标学习,预测多种资源每小时发电的长期动态
Zhenghong Wang1, Yi Wang1, Furong Jia1
1Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, China.
概括
本研究介绍了ALI-GRU,这是一种用于长期发电预测的深度学习模型. 它通过平衡电网资源和减少浪费来改善可再生能源管理.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 气候科学 气候科学
背景情况:
- 电网不稳定和可再生能源浪费是由于发电不平衡造成的.
- 可再生能源的电网份额越来越大,需要平衡不同的发电源,因为气候依赖的变化.
- 长期预测对于管理电网稳定性和优化可再生能源整合至关重要.
研究的目的:
- 分析区域能源之间的交叉关系,并确定主要指标.
- 为全球能源互动提出一个综合模型 (ALI-GC).
- 开发一种新的深度学习模型 (ALI-GRU),用于长期协作电力生产预测.
主要方法:
- 在电网时间序列中识别表现出早期波动的领先指标.
- 适应领先指标-全球合 (ALI-GC) 模型的开发.
- 实施适应性领先指标带入的反复单元 (ALI-GRU) 深度学习模型进行预测.
主要成果:
- 利用了美国 (2018-2024) 的区域级小时电力生产数据.
- 阿里-格鲁实现了卓越的每小时端到端预测性能,超过了最先进的模型高达11.63%.
- 该模型在大规模的实时预测场景中表现出强大的适应性.
结论:
- 拟议的ALI-GRU模型为长期发电预测提供了显著的改进.
- 这种方法为加强可再生能源的管理和利用提供了实际的好处.
- 该研究强调了理解区域间能源动态对于电网稳定的重要性.
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