Solar energy collection power prediction method based on grey neural network

Abstract

The present invention discloses a solar energy collection power prediction method based on a grey neural network. The method comprises: selecting the original solar energy collection power at the sametime each day for a plurality of days and the factor data sequence affecting the solar energy collection, and using the grey predicting method to predict the solar energy collection power sequence toobtain preliminary prediction results; normalizing the grey prediction results and the original factor data sequence affecting the solar energy as the input of the neural network, taking the originalsolar energy data sequence as the output of the neural network, establishing a neural network model, and training the neural network until the neural network converges; and finally calling the trained neural network to perform the final solar energy collection power prediction. According to the method disclosed by the present invention, the grey modeling method and the neural network method are combined to establish the grey neural network model; and compared with the common neural network model, the grey prediction model is introduced to reduce the amount of calculation in the prediction andachieve relatively high accuracy in the case of a few samples, and the prediction accuracy is higher.

Description

technical field [0001] The invention relates to the field of sensor network energy collection, in particular to a method for predicting solar energy collection power based on a gray neural network. Background technique [0002] Wireless sensor networks (Wireless Sensor Networks, WSNs) are composed of a large number of sensors deployed in the monitoring area in a self-organizing and multi-hop manner, and cooperatively perceive, collect, process, and transmit wireless sensor information of monitoring objects in the network coverage area. The network is currently a frontier hot research field that has attracted much attention at home and abroad, involving a high degree of cross-discipline and highly integrated knowledge. It is considered to be one of the most influential technologies in the 21st century. [0003] The current wireless sensor network nodes are usually powered by micro batteries, and the energy is very limited. Effectively obtaining energy from the external enviro...

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Problems solved by technology

[0006] Aiming at the shortcomings of the high prediction error of the gray system model and the large amount of training data required by the neural network when the system is unstable, the present invention proposes a solar energy prediction method based on the gray neural network, which effectively solves the problem of small sample prediction and improves the prediction accuracy and accuracy of the model. Generalization

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