通过特征选择和人工神经网络增强光伏功率预测:一个案例研究
Mokhtar Ali1, Abdelhalim Rabehi1, Abdelkerim Souahlia1
1Telecommunications and Smart Systems Laboratory, University of Djelfa, P.O. Box 3117, 17000, Djelfa, Algeria.
Scientific reports
|July 2, 2025
概括
当与MLP和LSTM等人工神经网络相结合时,特征选择显著提高了光伏功率预测的准确性. 这有助于改善太阳能管理和电网稳定性.
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
- 数据科学用于电网的数据科学
背景情况:
- 对于可靠的可再生能源预测需求不断增长.
- 需要提高光伏 (PV) 输出功率预测的准确性.
- 由于太阳能的间歇性质,管理太阳能存在挑战.
研究的目的:
- 为了提高光伏 (PV) 功率预测的准确性.
- 系统地将特征选择技术与人工神经网络 (ANN) 结合起来.
- 为了确定光伏输出最相关的预测指标.
主要方法:
- 使用的特征选择方法:ReliefF,最小相关性,奇平方测试.
- 开发并测试了两个预测模型:多层感知器 (MLP) 和长短期记忆 (LSTM) 网络.
- 利用来自阿尔及利亚南部的真实世界光伏数据集.
主要成果:
- 特性选择显著提高了MLP和LSTM模型的预测准确度.
- 使用MLP的ReliefF实现了9.21%的正常化平均绝对误差 (nMAE) 和0.9608.8的R2.
- 用LSTM选择的奇平方特征导致nMAE为9.29%和R2为0.946.
结论:
- 仔细的特征选择提高了用于光伏预测的ANN模型性能.
- 特性选择减少了模型的复杂性,并提高了概括能力.
- 结果为有效的太阳能管理和电网稳定提供了宝贵的见解.
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