多标签机器学习用于在多个时间范围内预测连接到电网的光伏太阳能发电厂的功率
Amal A Hassan1, Doaa M Atia2, Hanaa T El-Madany2
1Photovoltaic Cells Department, Electronics Research Institute, Cairo, Egypt. amal.elramly@eri.sci.eg.
Scientific reports
|September 23, 2025
概括
使用机器学习,特别是随机森林 (RF),决策树 (DT) 和深度学习 (DL) 的精确光伏 (PV) 电力预测,提高了电网集成和运营安全. 这些模型可靠地在各种时间范围内预测光伏和交流功率输出.
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
- 电网管理 电网管理
背景情况:
- 太阳能发电的间歇性需要对电网稳定性进行准确的预测.
- 机器学习算法 (MLA) 为光伏 (PV) 功率预测提供了先进的解决方案.
- 可靠的预测对于高效的电力调度和电网安全至关重要.
研究的目的:
- 评估各种机器学习方法用于光伏和交流电力的多标签预测.
- 评估不同算法的性能,跨越多个时间和数据集.
- 确定最有效的模型来预测建筑应用的光伏电站发电量.
主要方法:
- 评估的算法:线性回归 (LR),多项式回归 (PR),神经网络 (NN),深度学习 (DL),梯度增强树木 (GBT),随机森林 (RF),决策树木 (DT),k-最近邻居 (k-NN) 和支持向量机器 (SVM).
- 利用一年的传感器数据 (太阳辐射,温度,风速) 进行培训和验证.
- 测试了24小时,一周,一个月和突然变化的预测性能,使用绝对误差 (AE),根平均平方误差 (RMSE) 和相关系数 (R) 等指标.
主要成果:
- 随机森林 (RF),决策树 (DT) 和深度学习 (DL) 显示出卓越的准确性 (R ≈ 99.8-100%) 与最小的错误 (RMSE ≈ 0.014-0.022).
- 这些模型显示出高适应性和可靠性,用于短期,中期和长期的光伏和交流功率预测.
- 准确的预测有助于电网运营商管理光伏功率的变化和优化逆变器效率.
结论:
- 射频,DT和DL对于多标签的光伏和交流功率预测非常有效.
- 准确的预测可以改善间歇性太阳能在电网中的整合.
- 该研究为电网运营商提供了宝贵的见解,并提高了太阳能发电系统的可靠性.
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