混沌局域法與神經(jīng)網(wǎng)絡(luò)組合供水量預(yù)測(cè)

孫曉婷+劉年?yáng)|+杜坤+周明+任剛紅

摘 要：城市供水量是非線性、非平穩(wěn)時(shí)間序列，組合預(yù)測(cè)模型能獲得更高精度預(yù)測(cè)結(jié)果。通過(guò)深入分析混沌局域法與神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)模型特點(diǎn)，提出了一種新的組合預(yù)測(cè)模型。首先，應(yīng)用混沌局域法對(duì)城市日供水量進(jìn)行初預(yù)測(cè)，然后，應(yīng)用神經(jīng)網(wǎng)絡(luò)對(duì)預(yù)測(cè)結(jié)果進(jìn)行修正。由于所提出的組合模型利用了混沌局域法及神經(jīng)網(wǎng)絡(luò)進(jìn)行優(yōu)勢(shì)互補(bǔ)，能同時(shí)提高預(yù)測(cè)精度與計(jì)算效率。為驗(yàn)證所提出組合預(yù)測(cè)模型的可行性，采用某市7 a實(shí)測(cè)供水量數(shù)據(jù)，對(duì)混沌局域法、BPNN、RBF及GRNN神經(jīng)網(wǎng)絡(luò)4種單一預(yù)測(cè)模型及相應(yīng)的3種組合模型預(yù)測(cè)精度進(jìn)行定量分析，結(jié)果表明，組合預(yù)測(cè)模型精度都高于對(duì)應(yīng)單一預(yù)測(cè)模型，混沌局域法與GRNN神經(jīng)網(wǎng)絡(luò)組合模型預(yù)測(cè)精度最高，且運(yùn)算時(shí)間遠(yuǎn)低于單一神經(jīng)網(wǎng)絡(luò)模型運(yùn)算時(shí)間。

關(guān)鍵詞：混沌局域法；神經(jīng)網(wǎng)絡(luò)；組合模型；日供水量預(yù)測(cè)

中圖分類(lèi)號(hào)：TP183

文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1674-4764（2017）05-0135-05

Abstract：Urban water supply is a nonlinear and non-stationary time series， and the combination forecasting model can get more accurate results. Through in-depth analysis of chaotic local-region method and neural network prediction model， this paper puts forward a new combination forecasting model， which uses chaotic local-region method to make a preliminary forecast for urban daily water supply， and then the prediction result is updated by neural network. The proposed combined model makes use of complementary advantages of the chaotic local-region method and the neural network， improving synchronously the accuracy and computational efficiency of the prediction results. To verify the proposed model， the prediction accuracy of the four single prediction models of Chaotic local-region method，BPNN， RBF and GRNN neural network and three corresponding combined models are analyzed quantitatively using seven years water supply data. The results show that combination forecasting model is of higher accuracy than single prediction model， and chaotic local-region method plus GRNN neural network combination model has highest accuracy with much lower computation time than single neural network predication model.

Keywords：chaotic local-region method； neural network； combination model； daily water supply forecast

城市供水量預(yù)測(cè)能輔助供水調(diào)度，提高水廠管理水平與生產(chǎn)效率，一直是學(xué)者們關(guān)注的重點(diǎn)課題[1-2]。供水量預(yù)測(cè)模型可分為傳統(tǒng)預(yù)測(cè)模型和基于新技術(shù)預(yù)測(cè)模型[3]，傳統(tǒng)模型需對(duì)數(shù)據(jù)序列性質(zhì)進(jìn)行假設(shè)，例如，平穩(wěn)性假設(shè)或周期性假設(shè)，若假設(shè)不合理，得出的預(yù)測(cè)模型則會(huì)嚴(yán)重失真；基于新技術(shù)的預(yù)測(cè)模型通過(guò)非線性、自適應(yīng)學(xué)習(xí)方法構(gòu)建模型，能克服傳統(tǒng)預(yù)測(cè)模型缺點(diǎn)。如Tiwari等[4]提出了一種基于小波技術(shù)的神經(jīng)網(wǎng)絡(luò)供水量短期預(yù)測(cè)模型，結(jié)果表明，其預(yù)測(cè)精度比傳統(tǒng)ARIMA、ARIMAX和WNN方法高。……