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As a kind of renewable energy, wind energy is getting more and more attention with its advantage of rich, clean and environmentally sustainable. Stochastic volatility is the inherent property of wind energy and the essential factor hindering the development of wind power prediction research. In this paper, by considering the actual movement rule of wind energy described by Lorenz system, the Lorenz system and the wavelet decomposition were combined to improve the wind speed prediction models of BP, RBF, and Elman neural network for the first time, and a wind speed prediction model with the Lorenz system based on wavelet decomposition was established. The model has been compared with three traditional numerical prediction methods, proving that Lorenz disturbance model can get better prediction accuracy and can grasp the actual movement of wind more accurately. The research of this paper can make up for the neglect of the atmospheric system in the field of wind speed prediction, which is helpful to the large-scale development and utilization of wind energy resources. [ABSTRACT FROM AUTHOR]

Wind power, as a new energy generation technology, has been applying widely and growing rapidly, which make it become the main force of renewable energy. However, wind speed sequence has its own character of the intermittent and uncertainty, which brings a great challenge to the safety and stability of the power grid, one of the valid ways solving the problem is improving the wind speed predicting accuracy. Therefore, given atmospheric disturbances, we firstly used empirical mode decomposition (EMD) to deal with the non-linear wind speed sequence, and combined with strong adaptive and self-learning ability of BP neural network, then, a wind speed prediction model, EMD-BP neural network based on Lorenz disturbance, was proposed. Finally, it was to made use of actual wind speed data to take a simulation experiment and explored the improvement effect of the preliminary forecasting sequence of wind speed influenced by Lorenz equation in the transient chaos and chaos. The results show that, the improved model weakened the random fluctuation of wind speed sequence, effectively corrected the wind speed sequences initial prediction values, and made a great improvement for the short-term wind speed prediction precision. This research work will help the power system dispatching department adjust the dispatching plan in time, formulate the wind farm control strategy reasonably, reduce the impact brought by wind power grid connection, increase the wind power penetration rate, and then promote the global energy power market innovation. [ABSTRACT FROM AUTHOR]

Wind power, as a potential new energy generation technology, is gradually developing towards to the mainstream energy in the world. However, the inherent random volatility of wind brings severe challenges to the safe operation of the grid and the reliability of power supply, one of the effective ways to solve the problem is to improve the accuracy of wind speed prediction. However, most of wind speed prediction model cannot well mine the inherent regularity of wind speed data. Therefore, this paper introduces variational mode decomposition (VMD) algorithm. And the Short-term Wind Speed Prediction Model based on GA-ANN improved by VMD is proposed, which can effectively improve the accuracy of wind speed prediction. Firstly, hierarchical cluster method in this paper is employed to extract the historical data with high similarity to the predicted day. And then the appropriate number of decompositions K is selected by judging the value of sample entropy, so that the extracted historical data is decomposed into K subsequences by the variational mode decomposition. Next, with the global optimization ability of genetic algorithm, the artificial neural network is optimized to improve the forecasting performance. Finally, the short-term wind speed forecasting model based on GA-ANN improved by VMD is employed to predict the wind speed of each subsequence and superimposed them to obtain the final wind speed prediction sequence. The results in this paper show that (1) the model can find the periodic fluctuation of wind speed through historical data by hierarchical cluster method, so that significantly improving the accuracy of short-term wind speed prediction; (2) for the wind speed prediction, the error value of GA-ANN model is smaller than that of BP neural network; (3) in view of the inherent nature of the wind, the model proposed in this paper can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results. The research work can help the relevant departments of the power system to accurately assess the risk of power grid operation, make a reasonable generation plan, effectively reduce the cost of power operation, and then greatly promote the development of green energy. • Proposing a new windspeedprediction model named HC-VMD-GA-BPmodel. • Using hierarchical cluster methodtoselect the historical wind speed data. • VMD is introduced to decomposethe original wind speed sequence. • Artificial neural networkisoptimized by GA to improve the prediction performance. • The HC-VMD-GA-BP model is comparedwith other models to verify itseffectiveness and high prediction accuracy. [ABSTRACT FROM AUTHOR]