本文選題：異常值檢測(cè) + 支持向量機(jī)回歸��； 參考：《蘭州大學(xué)》2017年碩士論文

【摘要】：建立模型的優(yōu)劣基于所搜集數(shù)據(jù)的質(zhì)量。由于各種不確定因素的影響,數(shù)據(jù)一般都存在異常值,所以對(duì)數(shù)據(jù)進(jìn)行異常值檢測(cè)是十分必要的。此外,對(duì)時(shí)間序列數(shù)據(jù)進(jìn)行分解也是比較常用的一種數(shù)據(jù)預(yù)處理方法,分解后的子序列能夠反映出數(shù)據(jù)的不同特征,可為數(shù)據(jù)的進(jìn)一步分析提供準(zhǔn)確的信息。本文以風(fēng)速作為研究對(duì)象。一方面,風(fēng)力資源是清潔能源,另一方面,風(fēng)力資源為風(fēng)能發(fā)電提供了充足的來源,屬于可持續(xù)能源,所以對(duì)風(fēng)速的預(yù)測(cè)就顯得十分重要。本文基于西班牙Sotavento Galicia風(fēng)場(chǎng)的風(fēng)速數(shù)據(jù),首先運(yùn)用支持向量機(jī)回歸(Support Vector Regression, SVR)方法對(duì)原始數(shù)據(jù)進(jìn)行異常值檢測(cè)。然后運(yùn)用變分模態(tài)分解(Variational Mode Decomposition, VMD)方法,對(duì)剔除異常值后的序列進(jìn)行分解。接下來運(yùn)用廣義回歸神經(jīng)網(wǎng)絡(luò)(Generalized Regression Neural Network, GRNN)對(duì)分解后的子序列分別進(jìn)行預(yù)測(cè)。最后將所有子序列的預(yù)測(cè)值進(jìn)行相加并運(yùn)用馬爾科夫(Markov)過程進(jìn)行誤差修正得到最終的預(yù)測(cè)值。實(shí)驗(yàn)結(jié)果表明,SVR異常值檢測(cè)方法、VMD分解方法和Markov誤差修正模型對(duì)于模型預(yù)測(cè)精度的提高是有用的。此外,本文將提出的模型與其它常規(guī)方法進(jìn)行了對(duì)比,證明了其效率和估計(jì)性能。
[Abstract]:The establishment of the model is based on the quality of the collected data.Because of the influence of various uncertain factors, there are outliers in the data, so it is necessary to detect the outliers.In addition, decomposing time series data is also a commonly used method of data preprocessing. The decomposed sub-sequences can reflect the different characteristics of the data and provide accurate information for the further analysis of the data.This paper takes the wind speed as the research object.On the one hand, wind energy is a clean energy, on the other hand, wind resources provide sufficient sources for wind power generation, so it is very important to predict wind speed.In this paper, based on the wind speed data of Sotavento Galicia wind field in Spain, the support vector machine regression support Vector method is used to detect the outliers of the original data.Then the variational mode decomposition (VMD) method is used to decompose the sequence after eliminating the outliers.Then generalized Regression Neural network (GRNN) is used to predict the decomposed subsequences.Finally, the prediction values of all sub-sequences are added together and the final prediction value is obtained by using Markov Markov process to correct the error.The experimental results show that the VMD decomposition method and the Markov error correction model are useful for improving the prediction accuracy of the model.In addition, the proposed model is compared with other conventional methods, and its efficiency and estimation performance are proved.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM614;P412.16

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本文編號(hào)：1754844