Title: Combined forecasting model of urban water consumption based on adaptive filtering and BP neural network

Authors: Fuchen Ban; Dan Wu; Yueming Hei

Addresses: Faculty of Municipal and Environmental Engineering, Shenyang Construction University, 9 Hunnan Avenue, Hunnan District, Shenyang City, Liaoning Province, China ' Faculty of Municipal and Environmental Engineering, Shenyang Construction University, 9 Hunnan Avenue, Hunnan District, Shenyang City, Liaoning Province, China ' Faculty of Municipal and Environmental Engineering, Shenyang Construction University, 9 Hunnan Avenue, Hunnan District, Shenyang City, Liaoning Province, China

Abstract: In order to solve the problem of improving the precision of urban short-term water consumption forecasting, the idea of combination forecasting is put forward. According to the water use data of a city, the time series prediction method and the explanatory prediction method are used to forecast the water use in the short-term. In order to combine the advantages of the two forecasting methods, this paper proposes a combination forecasting method based on weight coefficient optimisation theory. Compared with the single prediction model, the combined forecasting model has higher accuracy and stability.

Keywords: water demand prediction; adaptive filtering method; BP neural network method; combined forecasting model.

DOI: 10.1504/IJSHC.2018.095011

International Journal of Social and Humanistic Computing, 2018 Vol.3 No.1, pp.34 - 45

Received: 25 Jan 2017
Accepted: 05 Apr 2018
Published online: 28 Sep 2018 *