Water supply prediction method and water supply prediction system

Abstract

The invention discloses a water supply prediction method and a water supply prediction system. The water supply prediction method is characterized in that historical data is analyzed, and a water supply prediction model is established, and a functional relation between an input layer parameter and a water supply quantity output by an output layer is acquired; real-time water supply quantity prediction is carried out according to the established water supply prediction model and required prediction date; the processes of analyzing the historical data and establishing the water supply prediction model comprise but not limited to the process of preprocessing the historical data by adopting a moving average method, the process of determining the network topological structure of the water supply prediction model by adopting the neural network algorithm, and the process of determining the water supply prediction model according to the preprocessed historical data and the corresponding network topological structure. The moving average method and the neural network algorithm are used for the water supply quantity prediction, and therefore calculation complexity is low, obvious periodicity and strong non-linearity are provided, real-time performance is good, the influences of various factors on the water supply quantity are described accurately, the convergence speed of the neural network model is accelerated, and the water supply prediction method and the water supply prediction system are widely used for the water supply analysis field.

Description

technical field [0001] The invention relates to the field of water supply analysis, in particular to a water supply prediction method and system. Background technique [0002] Urban water supply forecasting refers to the use of past information to calculate the urban water supply load for a certain period of time in the future. Urban water supply forecasting is an important preliminary work for urban water supply system planning, construction and renovation optimization. Its accuracy will directly affect the size of the project, project cost, reliability and practicability of scheduling decisions. [0003] Urban water supply forecasting can be broadly classified into two types in terms of time: short-term water supply forecasting and medium- and long-term water supply forecasting. The former refers to predicting the water supply in a short period of time in the future (such as hourly forecast, daily forecast or weekly forecast) based on historical water supply data combined...

AI Technical Summary

Problems solved by technology

However, these existing water supply prediction methods have relatively large defects or deficiencies: the exponential smoothing model requires a large amount of data, and the calculation complexity is high; The linear feature is weakened to a certain extent; the autoregressive (AR) forecasting model has obvious hysteresis. When the actual data changes abnormally, the forecast data cannot reflect the abnormal change due to the smoothing effect of the model, making it predict some abnormalities. Large errors or even distortions are caused when the value is large; the gray forecasting model is difficult to accurately describe the influence of various factors on the water supply; the traditional neural network model converges slowly and is easy to fall into the situation of local minimum

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