并行增强神经网络,相互信息,用于前一天的太阳辐射预测
Ubaid Ahmed1, Anzar Mahmood1, Ahsan Raza Khan2
1Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur, 10250, Pakistan.
Scientific reports
|April 4, 2025
概括
这项研究引入了一种新的并行增强神经网络 (PBNN),用于准确预测太阳辐射量,提高光伏系统的可靠性. 通过将多个决策树算法与神经网络相结合,PBNN框架提高了预测准确性.
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
- 计算科学 计算科学
背景情况:
- 全球向可持续能源的转变需要可靠的可再生能源,如太阳能.
- 太阳能的间歇性对光伏 (PV) 系统集成提出了挑战,需要准确的太阳辐射 (SI) 预测.
- 现有的深度学习 (DL) 和决策树 (DT) 模型在计算成本,数据要求和通用性方面存在局限性.
研究的目的:
- 开发一种新的并行增强神经网络 (PBNN) 框架,以克服传统SI预测模型的局限性.
- 提高集成光伏系统太阳辐射预测的准确性和可靠性.
- 提高预测算法的效率和通用性.
主要方法:
- 一个混合PBNN框架,集成提升算法 (极端梯度提升,分类提升,随机森林) 与前神经网络 (FFNN).
- 基础学习者输出 (DT算法) 的并行处理,随后由FFNN对最终预测进行加权.
- 相互信息 (MI) 算法用于特征选择以确定关键的SI预测变量.
主要成果:
- PBNN框架在预测准确度方面取得了显著的改进,在伊斯兰堡和圣地亚哥数据集中,平均绝对百分比误差 (MAPE) 分别减少了[公式:见文本]和[公式:见文本].
- 使用MI算法的特征选择提高了PBNN模型的性能.
- 通过文献比较的稳定性分析证实了拟议的PBNN的有效性.
结论:
- 与现有方法相比,拟议的PBNN框架为太阳辐射预测提供了一种优越的方法.
- PBNN有效地解决了与传统DL和DT模型相关的计算和通用性问题.
- 使用PBNN准确的SI预测对于太阳能系统的可靠和高效运行至关重要.
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