Wind Power Prediction Method Based On Deep Data Mining

With the proposal of the national ’carbon neutralization’ and ’carbon peak’ plans,the construction,installation,and grid connection of more and more wind turbines have been promoted.Wind power prediction is a key factor for the power grid to adjust the operation strategy.Accurate wind power prediction can provide a reference for power grid dispatching and operation,which is conducive to the power grid absorbing the power generation of wind turbines and reducing fossil fuel consumption to reduce carbon emissions.However,the prediction accuracy of wind power prediction is limited by the wind power’s randomness and volatility.Aiming at the problem that the current wind power prediction accuracy is low and cannot fully meet the requirements of power grid dispatching,a double-layer decomposition prediction method of wind power based on the complementary ensemble empirical mode decomposition(CEEMD)and one-dimensional convolution neural network algorithm(1DCNN)is proposed.The contributions of this paper are as follow:(1)A short-term wind power prediction model based on 1DCNN is constructed.Firstly,the wind power prediction is analyzed in principle,and the wind speed is determined as the input variable of wind power;Then,data cleaning of wind power;Finally,the 1DCNN algorithm is used to predict the wind power,and compared with multi-layer perceptron,support vector machine and autoregressive comprehensive moving average model.The results verify the effectiveness of the lDCNN algorithm for wind power prediction.(2)A short-term wind power prediction method based on CEEMD double decomposition and 1DCNN is proposed.After the wind power and wind speed are decomposed by CEEMD algorithm,the high correlation components are selected by mutual information method.According to the sequence selection results,1DCNN is used to predict each wind power component,and the final wind power prediction value is obtained by component integration method.According to the prediction results of the model,under the wind power prediction method based on deep data mining proposed in this paper,the root mean square error and average absolute percentage error of the prediction results are lower than the traditional algorithm,which verifies the effectiveness of the wind power prediction method proposed in this paper.In this paper,a two-level decomposition method of wind power is proposed,which deeply excavates the internal relationship between wind power and wind speed data,effectively improves the prediction accuracy of wind power,and develops a wind power prediction system based on this method,which can be used for short-term prediction of wind power in wind farms.