使用可再生资源的概率和系统数据处理模型进行电力分配和预测
Hammad Alnuman1, Ghulam Abbas2, Amr Yousef3,4
1Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia. hhalnuman@ju.edu.sa.
Scientific reports
|July 27, 2025
概括
概率系统处理方法 (PSPM) 提高了可再生能源的预测和分配. 这种方法提高了精度20%,效率25%,并减少了性能源系统的延迟35%.
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
- 数据科学数据科学数据科学
背景情况:
- 由于不可预测的输出功率和波动,可再生能源系统面临着挑战.
- 目前的预测方法与需求高峰扎,导致效率低下和不稳定.
- 准确的输出功率估计和分配管理对于广泛采用可再生能源至关重要.
研究的目的:
- 引入概率系统处理方法 (PSPM) 以改进短期需求预测和发电分配管理.
- 实时增强能源生产和分配状态之间的平衡.
- 在可再生能源系统内动态检测和区分不适当的电力分配激增.
主要方法:
- 概率系统处理方法 (PSPM) 使用基于奖励的状态模型学习.
- 它结合了实时和历史数据,包括消费,高峰产能和断电,以积极预测需求.
- 验证是使用ARRA项目中的智能电网数据集进行的.
主要成果:
- 与现有方法相比,PSPM显示预测成功率有20%的改善.
- 通过应用PSPM,分销效率提高了25%.
- 分析延迟降低了35%,显示了增强的操作速度.
结论:
- PSPM提供了一种新的方法来提高可再生能源系统的弹性和运营效率.
- 该方法将概率分析与强化学习相结合,解决了适应性能量分布研究中的差距.
- PSPM是实用的,可扩展的,在可持续电力基础设施自动化,能源政策和智能电网管理方面有潜在的应用.
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