基于S-BGD和梯度累积策略的改进深度学习方法及其在光伏出力预测中的应用Improved Deep Learning Algorithm Based on S-BGD and Gradient Pile Strategy and Its Application in PV Power Forecasting
黎静华;黄乾;韦善阳;黄玉金;
摘要(Abstract):
为提高光伏出力的预测精度,提出了一种改进深度学习算法的光伏出力预测方法。首先,针对传统的深度学习算法采用批量梯度下降(batch gradient descent,BGD)法训练模型参数速度慢的问题,利用随机梯度下降(stochastic gradient descent,SGD)法训练快的优点,提出了一种改进的随机-批量梯度下降(stochastic-batch gradient descent,S-BGD)搜索方法,该方法兼具SGD和BGD的优点,提高了参数训练的速度。然后,针对参数训练过程中容易陷入局部最优点和鞍点的问题,借鉴运动学理论,提出了一种基于梯度累积(gradient pile,GP)的训练方法。该方法以累积梯度作为参数的修正量,可以有效地避免训练陷入局部点和鞍点,进而提高预测精度。最后,以澳大利亚艾丽斯斯普林光伏电站的数据为样本,将所提方法应用于光伏出力预测中,验证所提方法的有效性。
关键词(KeyWords): 光伏出力预测;深度学习算法;梯度下降法;梯度累积量;参数训练;神经网络;随机-批量梯度下降
基金项目(Foundation): 国家重点研发计划支持项目(2016YFB0900100);; 国家自然科学基金项目资助(51377027)~~
作者(Author): 黎静华;黄乾;韦善阳;黄玉金;
Email:
DOI: 10.13335/j.1000-3673.pst.2017.1393
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