霍德里克-普雷斯科特波器 (Hp-波器) 和波形变换 (WT) 与优化支持向量机 (PSO-SVM) 的新集成,用于预测太阳辐射
Shuvendu Pal Shuvo1, Shirshendu Pal Shibazee2, Goutam Paul3
1Department of Civil Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh. shuvenduce@gmail.com.
Scientific reports
|April 25, 2025
概括
这项研究引入了一种用于太阳辐射预测的新型多混合模型,通过整合霍德里克-普雷斯科特波器,离散波形变换和支持矢量机来显著提高准确性,以便更好地分析气候数据.
科学领域:
- 环境科学 环境科学
- 气候建模气候模型
- 数据科学数据科学数据科学
背景情况:
- 太阳辐射预测是复杂的,因为非线性和杂的气候数据.
- 现有的混合模型结合波形转换和机器学习提供了改进,但也有局限性.
- 准确的太阳辐射预测对于可再生能源管理和气候研究至关重要.
研究的目的:
- 开发和评估一种新的多混合模型,用于增强太阳辐射预测.
- 为了应对太阳辐射数据中非线性和噪音气候模式所带来的挑战.
- 提高太阳辐射预测模型的精度和可靠性.
主要方法:
- 提出了一个多混合模型,集成霍德里克-普雷斯科特波器 (HP-Filter),离散波形变换 (DWT) 和支持向量机 (SVM).
- 使用了孟加拉国气象局 (达卡和奇塔贡) 的数据,分为70%的培训,15%的验证和15%的测试集.
- 使用粒子集群优化来优化SVM超参数,在DWT之前应用HP-Filter以增强模式捕获.
主要成果:
- 与传统的SVM和混合DWT-SVM相比,拟议的多混合模型在平均平方误差 (MSE) 减少方面显著改善 (高达99.77%).
- 确定系数 (R2) 显示了显著的改善,比传统的SVM增加了多达54%,比混合DWT-SVM增加了4.40%.
- 该模型有效地捕获了太阳辐射数据中的复杂非线性趋势,表明了高精度和可靠性.
结论:
- 开发的多混合模型为太阳辐射预测提供了一个高度准确和可靠的方法.
- 集成HP-Filter,DWT和SVM有效处理非线性和杂的气候数据.
- 该模型显示了在不同地理区域应用的强大潜力,以有效预测太阳辐射.
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