Wind Power Combination Prediction Model Based on Time Series Decomposition and Machine Learning

Accurate wind power prediction results can improve the grid-connected capacity of wind power under the stable and secure operation of power grid. To improve the prediction accuracy of wind power, by means of integrating time series decomposition technology, machine learning and heuristic algorithm a dual-level combined prediction model for wind power was proposed. Firstly, a prediction model combining empirical mode decomposition technology with long- and short- term memory network (abbr. EMD-LSTM) was constructed. Meanwhile, a prediction model, in which the variational mode decomposition and simulated annealing algorithm (abbr. VMD-SA) were combined with deep belief network (abbr. DBN), was proposed. The constructed EMD-LSTM model and VMD-SA-DBN model were taken as the basic prediction models of the upper layer of the combined prediction model. Secondly, the extreme gradient boosting algorithm was used to construct the lower layer of the combined prediction model, and the prediction result from the two basic prediction model in the upper layer was input into the lower prediction model, to obtain the final prediction result. Finally, the effectiveness of the proposed algorithm was verified by measured data. Verification result shows that the prediction accuracy by the proposed two layer combined prediction model is higher than that from the single prediction model.