有贝叶斯密度估计模型的弹性网用于用于光伏能源预测的特征选择
Venkatachalam Mohanasundaram1, Balamurugan Rangaswamy2
1Department of Electrical and Electronics Engineering, Kongu Engineering College, Tamil Nadu, Perundurai, 638060, India. chalam203@gmail.com.
Scientific reports
|March 14, 2025
概括
本研究介绍了ELNET-BDE模型,用于准确预测光伏能源. 这种新的方法显著提高了预测准确性,提高了可再生能源在电网中的整合.
科学领域:
- 可再生能源系统可再生能源系统
- 机器学习应用 机器学习应用
- 统计建模 统计建模
背景情况:
- 准确的光伏 (PV) 能源预测对于将可再生能源 (RE) 整合到电网中至关重要.
- 传统的回归算法在PV预测中与多对线性和大数据集作斗争.
- 现有的方法往往缺乏最佳特征选择和对预测因素影响的强有力的处理.
研究的目的:
- 优化特征选择 (FS) 并提高对光伏发电的预测准确性.
- 解决多对线性挑战,提高光伏能源预测模型的可靠性.
- 提出和验证一种新的混合模型,ELNET-BDE,用于优越的光伏能源预测.
主要方法:
- 应用贝叶斯密度估计 (BDE) 结合弹性网 (ELNET) 回归分析.
- 使用ELNET进行特征选择和通过L1和L2处罚控制多线性.
- 整合ELNET的规范化与BDE的残余分布和预测因素影响的非参数预测.
主要成果:
- 拟议的ELNET-BDE模型在传统的机器学习 (ML) 算法 (如ANN,SVM,RF和GBM) 上表现出优异的性能.
- 与其他FS技术相比,预测错误的显著减少:根平均平方误差 (RMSE) 降低了高达15%,平均绝对误差 (MAE) 降低了高达20%.
- 该模型在印度维萨卡帕特南的大型数据集上得到验证,并结合了历史的光伏发电和气象因素.
结论:
- ELNET-BDE模型为光伏能源预测的准确性和可靠性提供了显著的改进.
- 这种先进的预测能力对于加强太阳能电网集成和优化整体可再生能源管理至关重要.
- 这些发现突显了该模型在现实世界能源分配系统中的实际应用潜力.
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