Wind speed prediction method and apparatus based on NARX neural network

Abstract

The invention discloses a wind speed prediction method and an apparatus based on an NARX neural network. The method includes: acquiring historical data of related parameters required by wind speed prediction, wherein the related parameters including wind speed, pitch angle, rotating speed, and power; performing normalization processing of the acquired data; inputting the processed data to the NARX neural network as a training sample for training; and inputting a test sample to the trained NARX neural network, performing reverse normalization of an output value, and obtaining a practical prediction value. According to the method and the apparatus, the model is built for the prediction of the wind speed by employing the NARX neural network, input parameters of the network are selected by employing formulas and principles of fan aerodynamics, the prediction is convenient, the accuracy is high, the wind energy capturing capability and the generating capacity are increased, and the method and the apparatus are applicable to be promoted and applied.

Description

technical field [0001] The invention relates to the technical field of wind power control, in particular to a wind speed prediction method and device based on a NARX neural network (non-linear autoregressive neural network). Background technique [0002] With the support of national industrial policies, China's wind power industry has experienced rapid growth in the past ten years. China has become a veritable wind power country. The great development of wind power and renewable energy is an irreversible trend in the future. In the past few years, the focus of wind power development has shifted from high wind speed to low wind speed areas. Through independent innovation, Chinese wind power complete machine enterprises have actively developed new models with larger unit capacity, larger impellers, lower wind speeds, and smarter models. Improve the development potential of wind resources and the level of wind energy utilization. Ultra-low wind speed fans with higher wind ener...

AI Technical Summary

Problems solved by technology

Patent CN103927460A, a short-term wind speed prediction method for wind farms based on RBF, uses RBF neural network, normalizes and denormalizes input and output data, takes temperature, humidity, air pressure and wind direction as input, but fails to make full use of Impact of historical data on forecasted wind speed

[0004] It can be seen that the above-mentioned existing wind speed prediction method obviously still has inconvenience and defects, and needs to be further improved urgently.

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