贝叶斯的方法用于建模和预测太阳能光伏发电
Mariana Villela Flesch1, Carlos Alberto de Bragança Pereira2, Erlandson Ferreira Saraiva3
1Faculty of Engineering, Architecture and Urbanism and Geography, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil.
这项研究引入了使用高斯过程的贝叶斯方法,以准确地建模和预测每日太阳能发电曲线. 该方法提供了流的函数估计,并表现出优异的性能与低误差率.
科学领域:
- 统计 统计 统计 统计
- 可再生能源的建模.
- 机器学习 机器学习
背景情况:
- 准确的太阳能预报对于电网管理至关重要.
- 传统的方法可能会与太阳能发电的固有变化和复杂模式作斗争.
- 贝叶斯式方法为时间序列建模中的不确定性量化提供了一个强大的框架.
研究的目的:
- 开发一个贝叶斯的方法来估计和预测每日太阳能发电曲线.
- 为了模拟太阳能发电的未知功能,使用高斯过程.
- 通过插值提供流的函数估计,以改善预测.
主要方法:
- 使用贝叶斯模型与高斯过程在每日太阳能价值之前.
- 采用吉布斯采样算法来估计模型参数,因为缺乏已知的后部分布形式.
- 通过从k变量正常分布中插入点来估计平滑函数.
主要成果:
- 建议的贝叶斯式方法有效地建模和预测太阳能发电曲线.
- 模拟研究和现实世界的数据应用显示了接近零的平均绝对百分比误差 (MAPE) 和根-平均-平方误差 (RMSE).
- 该方法产生了流的函数估计,表明高精度和可靠性.
结论:
- 贝叶斯方法与高斯过程是太阳能电力曲线估计和预测的高效工具.
- 吉布斯采样实现为复杂模型提供了准确的参数估计.
- 该方法显示了改善太阳能管理和集成到电网中的巨大潜力.
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