Composite quantile regression extreme learning machine with feature selection for short-term wind speed forecasting: A new approach. (1st November 2017)

• Record Type: Journal Article
• Authors: Zheng, Weiqin; Peng, Xiangang; Lu, Di; Zhang, Dan; Liu, Yi; Lin, Zhehao; Lin, Lixiang
• Is Part Of: Energy conversion and management. Volume 151(2017)
• Page Start: 737
• Page End: 752
• Publication Date: 2017-11-01
• Subjects: Wind speed forecasting -- Composite quantile regression outlier-robust extreme learning machine -- Feature selection -- Hybrid particle swarm algorithm -- Time adaptive filter based empirical mode decomposition; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2017.09.029
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Proposed a hybrid architecture based on CQR-ORELM model for wind speed forecasting. A hybrid PSOGSA is used for feature selection and parameter optimization. A TVF-EMD approach is firstly exploited to decompose the original wind speed signal. Predictive probabilistic density is estimated using different quantile results. 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.