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

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...

AI Technical Summary

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 neural network, which has the advantages of local approximation and global optimality, but it is easy to missing some key information

GRNN neural network, fast learning speed, easy to converge, but often falls into local minimum

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