Combined forecasting method of urban water demand based on minimum sum of square error

A combined prediction and error squared technology, applied in general water supply conservation, instrumentation, data processing applications, etc., can solve the problems of falling into local minima and easily losing key information.

Active Publication Date: 2019-01-18
HANGZHOU DIANZI UNIV
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Problems solved by technology

The demand for urban water supply has the characteristics of nonlinearity and random fluctuation. Although there are many prediction methods, each prediction method has its own advantages and disadvantages, such as RBF neur

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  • Combined forecasting method of urban water demand based on minimum sum of square error
  • Combined forecasting method of urban water demand based on minimum sum of square error
  • Combined forecasting method of urban water demand based on minimum sum of square error

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Embodiment Construction

[0104] In order to make the technical means and creative features realized by the present invention easy to understand, the embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings and examples.

[0105] The following combination figure 1 and figure 2 The present invention is further described.

[0106] In this example, a DMA area is considered, such as figure 2 . The DMA area includes two water inlets and four water outlets, of which the water inlet and outlet have flow measurements. Six flow data are collected through the SCADA system, and the water demand data is obtained in the DMA area after calculation. The sampling time of the obtained water demand is one hour, that is, there are 24 water demand data in one day, and the three-day water demand data and the four-day weather data (three-day weather data plus one-day weather forecast data) are used to predict the water demand for one day way to train ...

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Abstract

The invention discloses a combined forecasting method of urban water demand based on minimum sum of square error. At first, a water demand database of the water supply pipe network is established. Then the RBF neural network model, GRNN model and ARIMA model are trained and established. Finally, combined forecasting is carried out based on the minimum sum of square error. The invention combines the characteristics of RBF neural network, such as strong approximation ability and global optimization, with the characteristics of GRNN neural network, such as fast learning speed and easy convergence, and with the characteristics of ARIMA, such as flexibility and strong adaptability, and combines the rolling renewal strategy, so that the prediction method can dynamically adapt to the developmentand change of the environment.

Description

technical field [0001] The invention belongs to the field of urban water supply, and relates to a combined prediction method for urban water demand based on the minimum square sum of errors. Background technique [0002] Urban water demand forecasting is an important step in urban water resources management planning, and also one of the basic contents of regional water resources planning and optimal allocation. The demand for urban water supply has the characteristics of nonlinearity and random fluctuation. Although there are many prediction methods, each prediction method has its own advantages and disadvantages, such as RBF neural network, which has the advantages of local approximation and global optimality, but it is easy to Some key information is lost. GRNN neural network has fast learning speed and easy convergence, but it often falls into local minimum. [0003] The present invention proposes a new urban short-term water demand forecasting method—a linear combinati...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06
CPCG06Q10/06315G06Q50/06Y02A20/152
Inventor 徐哲沈佳辉陈晖何必仕孔亚广陈云
Owner HANGZHOU DIANZI UNIV
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