發(fā)布時間：2018-04-18 00:05

  本文選題：多步區(qū)間預(yù)測 + 聚類經(jīng)驗?zāi)B(tài)分解?樣本熵　； 參考：《電網(wǎng)技術(shù)》2016年07期

【摘要】：針對風(fēng)電功率序列的不確定性和隨機(jī)性特征,提出一種基于聚類經(jīng)驗?zāi)B(tài)分解-樣本熵和優(yōu)化極限學(xué)習(xí)機(jī)的多步區(qū)間預(yù)測模型。首先,利用聚類經(jīng)驗?zāi)B(tài)分解-樣本熵方法將原始風(fēng)電功率序列分解為一系列復(fù)雜度差異明顯的子序列。然后,分別對各子序列建立基于上下界直接估量的區(qū)間預(yù)測模型。為分析不同區(qū)間構(gòu)造的差異,提出一種體現(xiàn)訓(xùn)練目標(biāo)值偏離區(qū)間范圍影響的新型區(qū)間預(yù)測評估指標(biāo)作為目標(biāo)函數(shù),并采用基于混沌螢火蟲結(jié)合多策略融合自適應(yīng)差分進(jìn)化的優(yōu)化算法尋求其最優(yōu)解,以提高模型預(yù)測性能。最后,以某一風(fēng)電場實際功率數(shù)據(jù)為算例,驗證了所提模型能獲得可靠優(yōu)良的多步區(qū)間預(yù)測結(jié)果,可為風(fēng)電功率多步不確定性預(yù)測提供一種新的有效途徑。
[Abstract]:In view of the uncertainty and randomness of wind power series, a multi-step interval prediction model based on clustering empirical mode decomposition-sample entropy and optimal extreme learning machine is proposed.Firstly, the original wind power series is decomposed into a series of sub-sequences with obvious difference in complexity by cluster empirical mode decomposition-sample entropy method.Then, the interval prediction models based on the upper and lower bounds are established for each sub-sequence.In order to analyze the differences of different interval structures, a new type of interval prediction evaluation index is proposed as the objective function, which reflects the effect of the training target value deviating from the range of the interval.The optimization algorithm based on chaos firefly and multi-strategy fusion adaptive differential evolution is used to find the optimal solution to improve the prediction performance of the model.Finally, taking the actual power data of a wind farm as an example, it is verified that the proposed model can obtain reliable and excellent multi-step interval prediction results, and can provide a new effective way for wind power multi-step uncertainty prediction.
【作者單位】： 武漢大學(xué)電氣工程學(xué)院;國網(wǎng)湖北省電力公司經(jīng)濟(jì)技術(shù)研究院;
【基金】：國家重點基礎(chǔ)研究發(fā)展計劃項目(973項目)(2012CB215101) 國家自然科學(xué)基金項目(51309258)~~
【分類號】：TM614

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本文編號：1765888