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使用猎优化器和随机森林改进太阳辐射预测
Ibrahim Al-Shourbaji1,2, Pramod H Kachare3, Abdoh Jabbari1
1Department of Electrical and Electronics Engineering, Jazan University, Jazan, Saudi Arabia.
PloS one
|December 20, 2024
概括
一个新的猎优化器-随机森林 (CO-RF) 模型准确地预测可再生能源的太阳辐射 (SR). 与其他方法相比,这种机器学习方法显著减少了预测错误.
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
- 环境监测 环境监测
背景情况:
- 准确的太阳辐射 (SR) 预测对于高效的可再生能源发电和热系统至关重要.
- 机器学习 (ML) 模型为复杂的SR预测任务提供高精度和计算效率.
- 现有的ML模型需要优化特征选择以提高预测准确度.
研究的目的:
- 引入一种创新的SR预测模型,即猎优化器-随机森林 (CO-RF).
- 使用猎优化器 (CO) 进行最佳特征选择,用于每小时的SR预测.
- 评估CO-RF模型的性能与已建立的ML技术相比.
主要方法:
- 开发了一种混合模型,集成了猎优化器 (CO) 进行特征选择和随机森林 (RF) 进行预测.
- 将CO-RF模型应用于两个不同的公开可用的太阳辐射数据集.
- 通过使用平均绝对误差 (MAE),平均平方误差 (MSE) 和R平方 (R2) 度量来验证模型性能.
主要成果:
- 在两种数据集中,CO-RF模型在训练和测试阶段都表现出卓越的性能.
- 在第一个数据集上实现了0.0365的低MAE和0.0074的MSE,R2为0.9251.
- 在第二个数据集上获得了0.0469的MAE和0.0032的MSE,在第二个数据集上R2为0.9868的R2,这表明错误显著减少.
结论:
- 拟议的CO-RF模型显著优于物流回归,支持矢量机,人工神经网络和独立的随机森林.
- CO-RF模型为每小时的太阳辐射预测提供了一个高度准确和可靠的解决方案.
- 这一进步支持提高太阳能系统的效率和可靠性.
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