系统级可再生能源和电力需求的概率前一天预测
Guillermo Terrén-Serrano1,2, Ranjit Deshmukh3,4,5, Manel Martínez-Ramón6
1Environmental Studies, University of California Santa Barbara, Santa Barbara, CA, USA. guillermoterren@ucsb.edu.
Nature communications
|March 1, 2026
概括
准确的前一天电力需求和可再生能源预测对于电网稳定性至关重要. 新的概率机器学习模型将预测准确度提高25%,提高电网可靠性和市场效率.
科学领域:
- 电力系统工程 电力系统工程
- 机器学习 机器学习
- 整合可再生能源的整合
背景情况:
- 越来越多的风能和太阳能发电增加了电网运行不确定性.
- 准确的前一天电力需求和可再生能源发电预测对于电网可靠性和成本效益至关重要.
研究的目的:
- 开发和评估先进的预测模型,以改善前一天的电力需求和可再生能源发电预测.
- 评估概率预测对经营储备分配和电网稳定性的影响.
主要方法:
- 开发了多种前一天预测模型,将机器学习用于天气变量识别和概率方法用于不确定性量化.
- 在加利福尼亚独立系统运营商的数据上使用适当的评分规则评估模型性能.
- 将概率联合分布预测与传统的决定性方法进行了比较.
主要成果:
- 与现有基准相比,表现最好的模型在预测技能方面表现出25%的改进.
- 基于联合分配的概率预测导致了更有效的经营储备分配.
- 这些模型利用公开可用的天气数据进行预报.
结论:
- 概率机器学习为高可再生能源透率的电力系统提高市场效率和电网稳定性提供了巨大的潜力.
- 共同的概率分布预测大大提高了系统级预测性能.
- 开发的模型为更可靠和更具成本效益的电网运营提供了途径.
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