Reservoir optimal operation method based on neural network model

Abstract

The invention discloses a reservoir optimal operation method based on a neural network model. For the nonlinearity and complexity of a reservoir group operation function, the main branch- neural network technology in computational intelligence is adopted, and a reservoir group is modeled. The method specifically comprises the steps that a target function is determined at first for reservoir operation, then an artificial neural network system is built between a target variable and variables influencing the target variable, actually-measured data are selected as samples, the artificial neural network achieves continuous learning according to a certain learning rule, complex nonlinear mapping is built between input and output, then other data are used as predicted samples to be input into a well-trained network, and therefore target variable prediction or function fitting can be conducted. Due to the facts the neural network directly achieves learning from the actually-measured data samples and time needed by the well-trained neural network during an application is short, requirements for instantaneity of reservoir operation are met, operation efficiency and learning precision and speed are greatly improved.

Description

technical field [0001] The invention relates to a method for optimal dispatching of a reservoir based on a neural network model. Since the neural network learns directly from the measured data samples, and the time required for the trained neural network to be applied is very short, it is very in line with the real-time requirements of reservoir scheduling, greatly improving the scheduling efficiency, learning accuracy and speed. Background technique [0002] Reservoir scheduling is a comprehensive application control technology for reservoirs. According to the different water conservancy and hydropower tasks undertaken by different types of reservoirs, it is constrained by the necessary conditions and rules for specific reservoir operation, and on the premise of ensuring the safety of dams and downstream flood control. Relying on the storage capacity of the reservoir, the inbound water volume of the reservoir is adjusted reasonably to achieve the purpose of comprehensively ...

AI Technical Summary

Problems solved by technology

[0006] Due to the nonlinearity and complexity of the reservoir scheduling problem itself, the application of computational intelligence algorithms in reservoir scheduling problems is becoming more and more extensive. Among them, a branch of computational intelligence—the neural network algorithm has a good role in the field of simulating optimal reservoir scheduling schemes. performance, but the algorithm currently has a long convergence time and is easily trapped in local optimum application limitations

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