Wind power field power prediction method based on deep neural network
A deep neural network and power forecasting technology, applied in the field of power system forecasting and control, can solve the problems that the wind power output forecasting system cannot be directly applied, the forecasting accuracy needs to be tested and improved, and the effect is not satisfactory. Accuracy, the effect of reducing the pressure of grid connection
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[0022] The present invention provides a wind farm power prediction method based on a deep neural network. The present invention will be described in detail below with reference to the accompanying drawings and embodiments.
[0023] The present invention addresses the problem of low accuracy of wind farm power prediction in the related art. In the embodiment, the factors affecting the wind farm power are used as the input of the deep neural network model, and the wind farm power is predicted through deep learning. The solution of the embodiment is described in detail below.
[0024] The deep neural network used in this embodiment is an auto-encoder network structure, which is a network based on 7-layer nonlinear mapping.
[0025] figure 1 It is the overall flow chart of the wind farm power prediction method based on deep neural network, including four steps:
[0026] Step a. The weather forecast value provided by the numerical weather forecast system, specifically to obtain the 72-hour...
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