走向能源效率:基于深度学习的光伏发电预测策略的全面审查
Mauladdawilah Husein1, E J Gago1, Balfaqih Hasan2
1University of Granada, School of Civil Engineering, 18001, Granada, Spain.
Heliyon
|July 25, 2024
概括
对光伏的时间序列预测需要标准化的数据集和指标. 本综述分析了36项最近的研究,强调了模型类型和评估指标,以指导未来的研究.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 时间序列预测,特别是光伏 (PV) 能源,缺乏标准化的基准数据集和性能指标,阻碍了进展.
- 现有的光伏预测模型通常在不同的环境中开发,并进行非标准化的评估,这使得直接比较变得困难.
- 光伏数据的独特,特定领域的性质进一步复杂化了普遍适用的预测模型的开发.
研究的目的:
- 系统地审查使用深度学习模型进行光伏时间序列预测的最新技术.
- 根据模型类型,超参数,数据源和评估指标分析和比较36项最近的研究.
- 为了确定当前的趋势,挑战和未来的研究方向在该领域.
主要方法:
- 从Web of Science和Scopus数据库中对2022-2023年发表的文章进行系统的文献综述.
- 使用关键词过研究:"光伏"",深度学习"",预测"和"时间序列".
- 基于统计分析,模型架构 (ANN,RNN,CNN,变压器) 和评估指标 (RMSE,MAE,MAPE,NRMSE,R2) 的36个选定的案例研究的分类和比较.
主要成果:
- 对36项研究的分析揭示了不同的数据来源,输入/输出视野和模型实现.
- 根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 是常用的,但缺乏普遍的可比性.
- 很大一部分 (33%) 的研究采用了普遍指标,如平均绝对百分比误差 (MAPE),规范化根平均平方误差 (NRMSE) 和确定系数 (R2),表明向标准化迈进.
结论:
- 太阳能预测领域需要通用错误指标来进行有效的全球基准测试.
- 标准化数据集和评估协议对于推进基于深度学习的光伏预测至关重要.
- 未来的研究应该专注于开发强大的模型和标准化的方法来克服当前的局限性.
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