An Improved ELM Model Based on CEEMD-LZC and Manifold Learning for Short-Term Wind Power Prediction

Abstract

The nonlinear characteristics of wind power series and random fluctuation characteristics of wind resources have a harmful effect on stability of wind power prediction. This paper proposes a novel hybrid wind power short-term prediction model for improving precision and stability of wind power prediction. Firstly, the non-stationary wind power time series is decomposed by complete ensemble empirical mode decomposition - Lempel-Ziv complexity (CEEMD-LZC). Secondly, local linear embedding (LLE) is used to reduce the dimension of meteorological data with maintaining the essential structure of meteorological data. The integrated meteorological data predigested by LLE are treated as input of the prediction model. Finally, ant lion optimization (ALO) is utilized to optimize the weights and biases of extreme learning machine (ELM) network nodes to improve ELM prediction performance. The results of case studies show that the proposed model has a good forecasting effect and is more advantageous in 48h-ahead wind power forecasting.