一种多层感知神经网络方法,用于优化中非地区的太阳辐射预测,利用气象洞察力
Inoussah Moungnutou Mfetoum1,2,3,4, Simon Koumi Ngoh5,6, Reagan Jean Jacques Molu5
1Technologies and Applied Sciences Laboratory, University Institute of Technology of Douala, University of Douala, P.O. Box: 8689, Douala, Cameroon. inoussah@gmail.com.
Scientific reports
|February 12, 2024
概括
这项研究使用人工神经网络 (ANN) 预测了杜阿拉的太阳辐射,达到98.883%的准确性. 这些发现支持在气候相似的地区开发可再生能源.
科学领域:
- 可再生能源工程可再生能源工程
- 气候科学 气候科学
- 人工智能的人工智能
背景情况:
- 中部非洲面临能源短缺,太阳能提供了一个可行的解决方案.
- 准确的太阳辐射数据至关重要,但由于气候变化,这是一个挑战.
- 预测倾斜表面的太阳辐射需要考虑气象因素.
研究的目的:
- 在喀麦隆杜阿拉的一个倾斜表面上预测太阳辐射.
- 研究气候变量 (温度,风速,湿度,气压) 对太阳辐射的影响.
- 开发和验证用于太阳辐射预测的人工神经网络 (ANN) 模型.
主要方法:
- 从杜阿拉的气象站收集的数据超过两年 (2019年1月 - 2020年10月) 在30分钟的间隔.
- 在Excel中进行数据预处理,然后在MATLAB中进行ANN模型开发.
- 一个ANN模型使用80%的数据进行训练,使用15%的数据进行验证,并使用5%的数据进行测试.
主要成果:
- 人工神经网络模型利用了物流的Sigmoid功能和50个隐藏层神经元,实现了高相关系数98.883%.
- 这表明观察到的和估计的太阳辐射量之间存在很强的一致性.
- 对不同输入数据组合的评估证实了模型的准确性.
结论:
- 开发的ANN模型准确地预测了杜阿拉的太阳辐射,考虑了关键的气候变量.
- 在类似的气候区域进行太阳辐射强度评估时,建议使用具有50个隐藏神经元的物流Sigmoid函数.
- 这项研究有助于改善中非的太阳能规划和部署.
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