使用实证和人工智能方法预测太阳辐射:一个比较性审查
Faisal Nawab1,2, Ag Sufiyan Abd Hamid3, Adnan Ibrahim1
1Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.
Heliyon
|July 24, 2023
概括
准确的太阳辐射预测对于太阳能和农业至关重要. 人工智能 (AI) 方法,特别是人工神经网络 (ANN),在太阳辐射预测方面表现出比实证模型更高的准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 太阳能照射数据对于太阳能项目的可行性和有效利用至关重要,影响能源发电和农业.
- 准确的太阳辐射预测对于选址,项目尺寸和优化作物选择至关重要.
- 由于成本和技术限制,太阳辐射的物理测量在全球范围内通常是不切实际的.
研究的目的:
- 对太阳辐射预测的实证和人工智能 (AI) 技术进行全面的审查和比较.
- 根据最新的科学文献,确定预测太阳辐射的最准确方法.
主要方法:
- 2017年至2022年间发表的研究文章的系统文献综述,重点关注太阳辐射预测.
- 实证模型 (例如,修改的基于阳光的模型) 和人工智能方法 (例如,人工神经网络,支持矢量机器) 的准确性和适用性的比较.
主要成果:
- 与实证方法相比,人工智能方法在太阳辐射预测中通常具有更高的准确性.
- 在实证模型中,修改的基于阳光的模型 (MSSM) 显示了最高的准确性,其次是基于阳光的模型 (SSM) 和非基于阳光的模型 (NSM).
- 人工神经网络 (ANN) 和混合模型在人工智能技术中实现了最高的准确性,主要输入包括温度,湿度,清晰度指数和降水.
结论:
- 基于人工智能的方法,特别是ANN,对于准确的太阳辐射预测更有效.
- 在没有阳光数据的情况下,实证模型,特别是NSM,可能是有价值的.
- 简单的经验模型可以准确地预测,而增加多项式顺序并不能显著改善结果.
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