LAUSR

Abstract With the growing wind penetration of wind resources into power generation worldwide, accurate and comprehensive wind speed forecasting (WSF) is becoming increasingly significant to ensure continuously economical and reliable power system operations. In this paper, a novel WSF framework based on composite quantile regression outlier-robust extreme learning machine (CQR-ORELM) with feature selection and parameter optimization using a hybrid population-based algorithm is developed. The CQR-ORELM model offers flexibility and efficiency to explore potential nonlinear characteristic among wind speed variables and improve model robustness and predictive capability. A hybrid algorithm with the combination of particle swarm optimization and gravitational search algorithm (PSOGSA) is utilized to fine-tune the optimal value of weights and bias in the ORELM network structure, while the binary version of PSOGSA (BPSOGSA) is exploited as a feature selection method to construct the most relevant input feature matrix for the model. Aimed to alleviate the influence of uncertainty in wind speed time series, a time adaptive filter based empirical mode decomposition (TVF-EMD) approach is used to decompose the original wind speed series into several intrinsic mode functions (IMFs). Each IMF is well-developed using the proposed method and the final forecasting results are obtained through aggregate calculation. This proposed wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed forecast strategy to predict wind speed is evaluated by the trials of 10 min ahead wind speed forecasting at two locations of the National Renewable Energy Laboratory (NREL). Comparative results confirm that the developed CQR-ORELM with feature selection procedure can describe the complete conditional distribution information hidden in variables and provide satisfactory wind speed information.