本文選題：發(fā)電量 + 徑流式水電站��； 參考：《中國農(nóng)村水利水電》2017年09期

【摘要】：預(yù)測徑流式水電站發(fā)量。針對徑流式水電站發(fā)電量變化的隨機(jī)性及水力發(fā)電系統(tǒng)的復(fù)雜非線性,提出一種基于相空間重構(gòu)小波神經(jīng)網(wǎng)絡(luò)的徑流式水電站發(fā)電量預(yù)測模型,并借助具有全局搜索能力的改進(jìn)粒子群優(yōu)化算法對小波神經(jīng)網(wǎng)絡(luò)特征參數(shù)進(jìn)行優(yōu)化,提高模型的精度和泛化能力。結(jié)果表明:相空間重構(gòu)小波神經(jīng)網(wǎng)絡(luò)模型的預(yù)測平均相對誤差約為8.7%,相關(guān)系數(shù)達(dá)到0.81,誤差分析指標(biāo)優(yōu)于傳統(tǒng)神經(jīng)網(wǎng)絡(luò)預(yù)測模型,在多步預(yù)測情形下模型的收斂性和穩(wěn)定性得到較為明顯增強(qiáng),能夠準(zhǔn)確進(jìn)行發(fā)電量預(yù)測。
[Abstract]:Prediction of runoff generation of hydropower stations.In view of the randomness of power generation of runoff hydropower station and the complex nonlinearity of hydropower system, a forecasting model of generating quantity of runoff hydropower station based on phase space reconstruction wavelet neural network is proposed.The improved particle swarm optimization algorithm with global searching ability is used to optimize the characteristic parameters of wavelet neural network to improve the accuracy and generalization ability of the model.The results show that the average relative error of the phase space reconstruction wavelet neural network model is about 8.7 and the correlation coefficient is 0.81.The error analysis index is better than the traditional neural network prediction model.The convergence and stability of the model are obviously enhanced in the case of multi-step prediction, which can accurately predict the generation capacity.
【作者單位】： 西北農(nóng)林科技大學(xué)水利與建筑工程學(xué)院;
【基金】：國家科技支撐計劃項目(2012BAD10B02) 中央高校基本科研業(yè)務(wù)費(fèi)專項資金(22050205) 陜西省水利科技項目(SLKJ-2013-14)
【分類號】：TV752

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