Ultra-Short Term Wind Speed Prediction Using K-Nearest Neighbor And Spatial Correlation Based On Historical Observations

Due to the tight supply of energy and the increasing of environmental pollution,more and more attention has been paid to the research of clean energy.Wind energy is a kind of high-quality clean energy,however,because of its instability,the application of wind power is subject to certain restrictions.Wind power prediction is an important way to solve the problem that wind power is difficult to parallel in the grid.In order to improve the accuracy and reliability of ultra-short term wind speed prediction,the k-nearest neighbor prediction of time series is generalized to the spatial correlation wind speed prediction according to the “mechanism model + identification model” strategy.Firstly,the wind speed upstream sites are sorted out through their wind directions to the predicted site(the local site),and their optimal lag time of wind speed to the local site is calculated.The reference vector of k-nearest neighbor prediction based on spatial correlation is a combination of the latest local wind speed historical observations and the upstream wind speed observations adjusted by its lag time.The k most similar neighbors of the reference vector are sought out from the wind speed historical observations by the Pearson product-moment correlation coefficient.The future wind speed of the local site is regressed by 7 models,include linear regression,partial least squares regression,least squares support vector machine regression,back propagation neural network,radial basis function neural network,generalized regression neural network and random forest.The numerical experiments of the prediction of wind speed of Huibertgat in the Netherlands in winter show that the linear regression,partial least squares regression and least squares support vector machine regression are more optimized regression models,and the optimal quantity of k-nearest neighbors is around 100.Using historical wind speed observations of 10 years gives a better result.The case studies support that the spatial correlation based on k-nearest neighbor can effectively use the similarities in historical data,then predict the ultra-short term wind speeds reliably.