Wind power prediction method based on modified particle swarm optimization BP neural network

Abstract

The invention discloses a wind power prediction method based on a modified particle swarm optimization BP neural network. The method includes the following steps: 1. encoding weight values and threshold values of a BP neural network as particles, and initializing the particles; 2. computing each particle fitness value with the difference between the result obtained from BP neural network training and an anticipated value as a fitness function; 3. comparing the fitness value of each particle and individual optimal particle to obtain a global optimal particle; 4. updating the speed and position of the particle; 5. determining whether the global particle meets termination conditions, if the global particle meets termination conditions, terminating the computing and outputting an optimal weight threshold value, and if the global particle does not meet termination conditions, back to step 2 and carrying out iterative operation; and 6. Using the optimal weight threshold value that is acquired by step 5 to connect an input layer, a hidden layer and an output layer of the BP neural network, and obtaining the result of wind power prediction on the basis of the result of the BP neural network. The method has fast convergence speed, high precision, and is not easily trapped to local extremum.

Description

technical field [0001] The invention relates to new energy predictive control, in particular to a wind power prediction method based on improved particle swarm optimization BP neural network. Background technique [0002] As a renewable energy source, wind energy has the advantages of no pollution, no need for fuel, and no occupation of cultivated land. It has attracted more and more people's attention, and its reserves are also very large. According to relevant statistics, the annual radiation energy from outer space is 1.5 *10 18 kWh, 2.5% of which is 3.8*10 16 kWh of energy is absorbed by the atmosphere, producing approximately 4.3*l0 l2 kWh of wind energy. my country's actual exploitable wind energy resource reserves are 253 million kW, accounting for 7.8% of the total wind energy resources at 10m altitude. [0003] According to the semi-annual report issued by the World Wind Energy Association, the global wind power industry newly installed 63,013MW in 2015, with an...

AI Technical Summary

Problems solved by technology

[0005] BP neural network (Back Propagation Neural Network, BPNN) is widely used in power generation prediction of wind power generation systems due to its good nonlinear mapping ability and self-adaptive learning ability. Both speed and accuracy are not ideal

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