發(fā)布時(shí)間：2018-06-18 19:21

  本文選題：風(fēng)速預(yù)測(cè) + 應(yīng)用研究��； 參考：《合肥工業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年11期

【摘要】：文章對(duì)持續(xù)法、BP神經(jīng)網(wǎng)絡(luò)和支持向量機(jī)(support vector machine,SVM)3種方法在提前24h風(fēng)速預(yù)測(cè)中的應(yīng)用進(jìn)行了研究和比較。為了消除季節(jié)對(duì)預(yù)測(cè)結(jié)果的影響,針對(duì)某年12個(gè)月份分別建立預(yù)測(cè)模型,結(jié)果表明:在大多數(shù)情況下,BP神經(jīng)網(wǎng)絡(luò)和SVM算法的預(yù)測(cè)結(jié)果要優(yōu)于持續(xù)法,并且SVM算法優(yōu)于BP神經(jīng)網(wǎng)絡(luò);但也有持續(xù)法優(yōu)于BP神經(jīng)網(wǎng)絡(luò)和SVM算法及BP算法優(yōu)于SVM算法的情況。因此不能絕對(duì)說某種算法優(yōu)于另一種算法,應(yīng)該根據(jù)具體情況來進(jìn)行分析判斷,或者通過組合預(yù)測(cè)來提高預(yù)測(cè)精度。
[Abstract]:In this paper, the application of continuous BP neural network and support vector machine SVM in 24 hours' wind speed prediction is studied and compared. In order to eliminate the influence of seasons on the prediction results, the prediction models are established for 12 months of a given year. The results show that in most cases, the prediction results of BP neural network and SVM algorithm are better than that of the persistence method. The SVM algorithm is superior to the BP neural network, but there are some cases where the persistence method is superior to the BP neural network and SVM algorithm and the BP algorithm is superior to the SVM algorithm. Therefore, it can not be absolutely said that one algorithm is superior to the other, and it should be analyzed and judged according to the specific situation, or the prediction accuracy should be improved by combination prediction.
【作者單位】： 國(guó)網(wǎng)安徽電力公司檢修公司;合肥工業(yè)大學(xué)教育部光伏系統(tǒng)工程研究中心;
【基金】：國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃(973計(jì)劃)資助項(xiàng)目(2009CB219708) 國(guó)家自然科學(xué)基金面上資助項(xiàng)目(51077033) 高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金資助項(xiàng)目(201301111110005) 廣東省引進(jìn)創(chuàng)新團(tuán)隊(duì)資助項(xiàng)目(2011N015)
【分類號(hào)】：TM614

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