本文選題：ARIMA模型 + 經(jīng)驗(yàn)?zāi)B(tài)分解�。� 參考：《沈陽農(nóng)業(yè)大學(xué)》2017年碩士論文

【摘要】：在風(fēng)力發(fā)電中,對風(fēng)功率的準(zhǔn)確預(yù)測可以降低旋轉(zhuǎn)備用容量,同時(shí)能夠給相關(guān)部門提供科學(xué)的電網(wǎng)調(diào)度方案,從而大大提高清潔能源的使用率。但是由于風(fēng)力發(fā)電伴隨的波動(dòng)性和不可控性,使風(fēng)功率數(shù)據(jù)具有非線性和非平穩(wěn)性,因此進(jìn)行精確的風(fēng)功率預(yù)測變得十分困難。時(shí)間序列中ARIMA模型可以根據(jù)風(fēng)功率數(shù)據(jù)的時(shí)序性進(jìn)行建模預(yù)測,但該模型在預(yù)測中隨著預(yù)測的步長增加會使精確度降低。論文將時(shí)間序列中的ARIMA預(yù)測模型與經(jīng)驗(yàn)?zāi)B(tài)分解法(Empirical Mode Decomposition,EMD)進(jìn)行了組合使用,使風(fēng)功率預(yù)測的精確度得到了一定的提高。在實(shí)例計(jì)算使用經(jīng)驗(yàn)?zāi)B(tài)分解算法時(shí),發(fā)現(xiàn)EMD分解法對序列分解過程中會出現(xiàn)模態(tài)混疊現(xiàn)象,為了解決該問題,本文對EMD算法進(jìn)行了一步步的改進(jìn),最終建立了改進(jìn)的集成經(jīng)驗(yàn)?zāi)B(tài)分解法(Modified Ensemble Empirical Mode Decomposition,MEEMD)。為了進(jìn)一步提高風(fēng)功率預(yù)測的精確度,論文將改進(jìn)的集成經(jīng)驗(yàn)?zāi)B(tài)分解法、ARIMA模型以及樣本熵(Sample Entropy,SE)模型的優(yōu)點(diǎn)進(jìn)行融合,建立了 MEEMD-SE-ARMA模型的混合算法。在論文中針對原始非平穩(wěn)的風(fēng)功率數(shù)據(jù)進(jìn)行超短期預(yù)測,使用MEEMD算法進(jìn)行分解,將分解后得到的多個(gè)風(fēng)功率子序列使用樣本熵進(jìn)行重組,分為一系列復(fù)雜度差異明顯的新風(fēng)功率子序列,使得到的新風(fēng)功率子序列接近平穩(wěn)數(shù)據(jù);利用時(shí)間序列的ARIMA模型對得到的每一個(gè)新風(fēng)功率子序列進(jìn)行建模預(yù)測,在建模過程中應(yīng)該充分的考慮時(shí)間序列中廣義自回歸條件異方差模型以及拉格朗日乘子檢驗(yàn),并分別建立對應(yīng)的ARIMA模型;將得到的各預(yù)測風(fēng)功率子序列進(jìn)行疊加重構(gòu),最終得到預(yù)測結(jié)果。論文分別建立了 ARIMA-GARCH 預(yù)測模型、EMD-ARIMA 模型、EEMD-ARIMA 模型以及MEEMD-SE-ARIMA模型,并將各預(yù)測模型的平均絕對百分誤差進(jìn)行了對比,最終證明建立的MEEMD-SE-ARMA混合算法可以有效的提高風(fēng)功率超短期預(yù)測的精確度。
[Abstract]:In wind power generation, the accurate prediction of wind power can reduce the rotation reserve capacity, and provide a scientific power grid scheduling scheme to the relevant departments, thus greatly improving the utilization rate of clean energy. However, due to the volatility and uncontrollability of wind power generation, the wind power data are nonlinear and non-stationary, so that the wind power data are not nonlinear and non-stationary. It is very difficult to predict the accurate wind power. In the time series, the ARIMA model can be modeled and predicted according to the timing of the wind power data, but the model can reduce the accuracy with the increase of the prediction step. The ARIMA prediction model in the time series and the Empirical Mode Decompositio method (Empirical) are used in the time series. N, EMD) have been used in combination to improve the accuracy of wind power prediction. When the empirical mode decomposition algorithm is used in the example calculation, it is found that the EMD decomposition method will have modal aliasing in the sequence decomposition process. In order to solve the problem, this paper has improved the EMD algorithm step by step, and finally established an improved set. Modified Ensemble Empirical Mode Decomposition (MEEMD). In order to further improve the accuracy of wind power prediction, the improved integrated empirical mode decomposition, ARIMA model and the advantage of sample entropy (Sample Entropy, SE) model are integrated, and a hybrid algorithm of MEEMD-SE-ARMA model is established. In this paper, the original non stationary wind power data are predicted by ultra short term, and the MEEMD algorithm is used to decompose. The multiple wind power subsequences are reorganized with the sample entropy, which can be divided into a series of new wind power subsequences with distinct differences in complexity, making the obtained new wind power subsequence close to the stationary data and using the time sequence. The ARIMA model is used to model and predict each new wind power subsequence. In the process of modeling, we should fully consider the generalized autoregressive conditional heteroscedasticity model and the Lagrange multiplier test in the time series, and establish the corresponding ARIMA model respectively. The prediction results are obtained. The ARIMA-GARCH prediction model, EMD-ARIMA model, EEMD-ARIMA model and MEEMD-SE-ARIMA model are established respectively, and the average absolute percentage error of each prediction model is compared. Finally, it is proved that the proposed MEEMD-SE-ARMA hybrid algorithm can effectively improve the accuracy of the ultra short term prediction of wind power.
【學(xué)位授予單位】：沈陽農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM614
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本文編號：2105167