Ultra-short-term wind power prediction method for offshore wind farms considering output fluctuation process
A technology for wind power forecasting and offshore wind power, which is applied in forecasting, neural learning methods, calculations, etc. It can solve the problems of output fluctuation, failure to take into account, and lack of generalization ability, and achieve the effect of improving convergence performance and forecasting accuracy.
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[0034] like figure 2 As shown, the present invention provides an ultra-short-term wind power prediction method for an offshore wind farm taking into account the output fluctuation process, which is characterized by comprising the following steps:
[0035] Step 1: Select the wind power data of the offshore wind farm, and delete the missing data and wrong data;
[0036] Step 2: Perform offshore wind power time series prediction model (OWTM) modeling on the original wind power data to obtain meteorological variables and meteorological fluctuation variables, and train an improved long-term recurrent convolutional neural network (LRCN) network to solve the OWTM;
[0037] Step 3: Randomly initialize and improve the weights of the LRCN network, set the maximum number of iterations K=50, and the current number of iterations k=1;
[0038] Step 4: Build a multi-convolution channel including a residual structure, input meteorological variables and meteorological fluctuation variables i...
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