LSTM short-term wind power prediction based on Affinity Propagation clustering algorithm
A Long Short Term Memory(LSTM)neural network wind power prediction model based on the Affinity Propagation(AP)Algorithm is proposed considering meteorological data and historical wind power data of wind farms.The NWP data and wind power data are clustered using the AP Algorithm to obtain sev-eral subsets of wind power data,at the same time,the Principal Components Analysis(PC A)is used to re-duce the dimensionality of the NWP subsets in order to obtain features that better reflect the wind power.Finally,the feature matching method is used to build a LSTM based neural network prediction model.The best matching model is selected for wind power prediction based on the NWP data of the prediction day and the power data of the previous day.Simulations were carried out using data from a wind power plant in Ningxia,and the experiments shows that the proposed method could improve the prediction accuracy of short-term wind power.
wind power predictionLong Short Term Memory(LSTM)Affinity Propagation clusteringfeature matchingPrincipal Component Analysis
万方数据
维普
