使用人工智能技术与不确定性分析进行季节性太阳辐射预测
V Gayathry1, Deepa Kaliyaperumal2, Surender Reddy Salkuti3
1Department of EEE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India.
Scientific reports
|August 2, 2024
概括
准确的太阳辐射预测对于可再生能源的整合至关重要. 这项研究使用人工智能 (AI) 结合点和间隔预测,通过量化预测不确定性来提高电网可靠性.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 电网整合 电网整合
背景情况:
- 将可再生能源整合到公用事业网中对于优化能源消耗至关重要.
- 准确预测可再生能源发电对于电网规划和稳定性至关重要.
- 现有的研究往往侧重于点预测,忽视预测的不确定性和可变性.
研究的目的:
- 开发和评估用于太阳辐射预测的人工智能 (AI) 技术.
- 将点预测与间隔预测相结合,提供全面的不确定性信息.
- 通过改进的预测方法,提高可再生能源整合的可靠性.
主要方法:
- 使用外源因子 (SARIMAX),支向量回归 (SVR) 和长短期记忆 (LSTM) 技术的季节性自动回归移动平均线进行太阳辐射预测.
- 使用不同季节 (冬季,夏季,季风,季风后) 的R平方值的预测模型的性能评估.
- 使用拉普拉斯分布匹配和不确定性评估通过置信区间和覆盖率进行预测错误分布分析.
主要成果:
- 支持向量回归 (SVR) 模型表现出卓越的性能,达到0.97 (冬季),0.96 (夏季) 和0.85 (季风和季风后) 的R平方值.
- 对SVR预测错误的拉普拉斯分布适配被证明是有效的,在所有季节的各种置信级别中获得了优异的覆盖率.
- 85%的信任率带给了89% (冬季),95% (夏季),90% (季风) 和88% (季风后) 的覆盖率.
结论:
- 该研究强调了将预测错误分布研究和建模纳入可靠间隔预测的意义.
- 结合点和间隔预测,可以更全面地了解预测不确定性,这对于电网管理至关重要.
- 使用SVR和拉普拉斯分布开发的方法通过提供可靠的太阳辐射预测来提高系统可靠性.
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