统计机器学习用于电流分析,考虑天气因素对光伏发电的影响
概括
使用生成对抗网络 (GAN) 的新统计机器学习模型准确模拟天气数据. 这增强了光伏发电和天气敏感负载的配电网络的电流分析.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 气象学 天气学
背景情况:
- 现代配电网络面临着由于高光伏 (PV) 集成和天气敏感负载的日益增加的天气变化影响.
- 准确的天气数据模拟对于可靠的电力系统分析和规划至关重要.
研究的目的:
- 开发和评估基于统计机器学习 (SML) 的新型随机天气发生器 (SWG),用于模拟每小时的天气数据.
- 评估拟议的SWG在模拟光伏发电和气候敏感负载的性能,以进行电流分析 (PFA).
主要方法:
- 拟议的SML模型整合了生成对抗网络 (GAN),概率理论和信息理论,以生成和评估模拟的每小时天气数据.
- 该GAN模型捕捉了天气不确定性,日间变化和季节性模式,在随机天气模拟中表现优于浅层学习模型.
- 该SWG被应用于模拟PV发电和PFA在真实配电网络上的天气敏感负载.
主要成果:
- 来自基于GAN的SWG的模拟数据在时间序列规律性,完整性和随机性方面与真实数据密切匹配.
- 基于GAN的SWG在动力流分析中,与浅层机器学习方法相比,显示出更高的准确性.
- 该模型成功地使用全年的模拟数据对与天气有关的电流进行了准确的分析.
结论:
- 拟议的基于GAN的SWG是用于电力系统中准确的随机天气模拟的有效工具.
- 这种方法为分析,规划和设计面临越来越多天气变化的配送网络提供了宝贵的见解.
- 该研究强调了深度学习模型在电力工程中复杂的天气模式模拟方面的优势.
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