基于自適應(yīng)過濾與BP神經(jīng)網(wǎng)絡(luò)的城市時用水量組合預(yù)測模型
發(fā)布時間:2018-07-15 11:40
【摘要】:城市短期時用水量預(yù)測是城市管網(wǎng)進行優(yōu)化調(diào)度決策的基礎(chǔ),用水量預(yù)測的準(zhǔn)確性與穩(wěn)定性更是關(guān)系著城市供水正常運行管理的關(guān)鍵。根據(jù)某市的用水?dāng)?shù)據(jù),分別利用時間序列預(yù)測方法以及解釋性預(yù)測方法對城市短期時用水量進行預(yù)測。綜合兩種預(yù)測方法的優(yōu)點,提出了基于權(quán)系數(shù)優(yōu)化理論的組合預(yù)測法。通過實例預(yù)測結(jié)果比較,用水量組合預(yù)測法模型較單項預(yù)測模型有更高的準(zhǔn)確性和穩(wěn)定性。
[Abstract]:Short-term urban water consumption prediction is the basis of optimal dispatching decision of urban pipe networks, and the accuracy and stability of water consumption prediction is the key to the normal operation and management of urban water supply. According to the water use data of a certain city, the time series forecasting method and the explanatory forecasting method are used to predict the short-term water consumption of the city. Combining the advantages of two forecasting methods, a combined prediction method based on weight coefficient optimization theory is proposed. By comparing the forecasting results with practical examples, the combined forecasting model of water consumption has higher accuracy and stability than the single forecasting model.
【作者單位】: 沈陽建筑大學(xué)市政與環(huán)境工程學(xué)院;
【基金】:住房和城鄉(xiāng)建設(shè)部2016年科技計劃項目(2016-K3-026) 遼寧省自然科學(xué)基金項目(20170540744) 遼寧省教育廳科技計劃項目(LJZ2016025)
【分類號】:TU991.31
[Abstract]:Short-term urban water consumption prediction is the basis of optimal dispatching decision of urban pipe networks, and the accuracy and stability of water consumption prediction is the key to the normal operation and management of urban water supply. According to the water use data of a certain city, the time series forecasting method and the explanatory forecasting method are used to predict the short-term water consumption of the city. Combining the advantages of two forecasting methods, a combined prediction method based on weight coefficient optimization theory is proposed. By comparing the forecasting results with practical examples, the combined forecasting model of water consumption has higher accuracy and stability than the single forecasting model.
【作者單位】: 沈陽建筑大學(xué)市政與環(huán)境工程學(xué)院;
【基金】:住房和城鄉(xiāng)建設(shè)部2016年科技計劃項目(2016-K3-026) 遼寧省自然科學(xué)基金項目(20170540744) 遼寧省教育廳科技計劃項目(LJZ2016025)
【分類號】:TU991.31
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