【摘要】：為提高光伏出力的預(yù)測精度,提出了一種改進深度學(xué)習(xí)算法的光伏出力預(yù)測方法。首先,針對傳統(tǒng)的深度學(xué)習(xí)算法采用批量梯度下降(batch gradient descent,BGD)法訓(xùn)練模型參數(shù)速度慢的問題,利用隨機梯度下降(stochastic gradient descent,SGD)法訓(xùn)練快的優(yōu)點,提出了一種改進的隨機-批量梯度下降(stochastic-batch gradient descent,S-BGD)搜索方法,該方法兼具SGD和BGD的優(yōu)點,提高了參數(shù)訓(xùn)練的速度。然后,針對參數(shù)訓(xùn)練過程中容易陷入局部最優(yōu)點和鞍點的問題,借鑒運動學(xué)理論,提出了一種基于梯度累積(gradient pile,GP)的訓(xùn)練方法。該方法以累積梯度作為參數(shù)的修正量,可以有效地避免訓(xùn)練陷入局部點和鞍點,進而提高預(yù)測精度。最后,以澳大利亞艾麗斯斯普林光伏電站的數(shù)據(jù)為樣本,將所提方法應(yīng)用于光伏出力預(yù)測中,驗證所提方法的有效性。
[Abstract]:In order to improve the accuracy of photovoltaic force prediction, an improved depth learning algorithm is proposed for photovoltaic force prediction. First of all, aiming at the problem that the traditional depth learning algorithm uses batch gradient descent (batch gradient descent,BGD) method to train the model parameters slowly, the advantage of the stochastic gradient descent (stochastic gradient descent,SGD) method is presented. An improved random-batch gradient descent (stochastic-batch gradient descent,S-BGD) search method is proposed, which combines the advantages of SGD and BGD, and improves the speed of parameter training. Then, aiming at the problem that parameter training is easy to fall into local optimum and saddle point, a training method based on gradient cumulation (gradient pile,GP) is proposed based on kinematics theory. In this method, the cumulative gradient is used as the parameter modifier, which can effectively avoid the training falling into local points and saddle points, and then improve the prediction accuracy. Finally, based on the data of Alice Spring photovoltaic power station in Australia, the proposed method is applied to photovoltaic force prediction to verify the effectiveness of the proposed method.
【作者單位】： 廣西電力系統(tǒng)最優(yōu)化與節(jié)能技術(shù)重點實驗室(廣西大學(xué));
【基金】：國家重點研發(fā)計劃支持項目(2016YFB0900100) 國家自然科學(xué)基金項目資助(51377027)~~
【分類號】：TM615
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本文編號：2360232