A novel wind power probabilistic forecasting approach based on joint quantile regression and multi-objective optimization. (April 2020)

• Record Type: Journal Article
• Authors: Hu, Jianming; Tang, Jingwei; Lin, Yingying
• Is Part Of: Renewable energy. Volume 149(2020)
• Page Start: 141
• Page End: 164
• Publication Date: 2020-04
• Subjects: Composite kernels -- Joint quantile estimation -- Multi-objective salp swarm optimization algorithm (MSSA) -- Wind forecasting; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.11.143
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Probabilistic forecasts of wind power generation play a significant role in operation management and decision-making for wind power producers and power system operators. This study introduces a novel joint quantile model based on the use of Mercer's kernels in the vector-valued RKHSs (Reproducing Kernel Hilbert Spaces) for the nonparametric probabilistic forecasts of wind power. The proposed approach takes the autocorrelation of the (nonlinearly) transformed input and output into consideration and improves the model flexibility by constructing composite kernels. Besides, the model is optimized by a new meta-heuristics algorithm named Multi-Objective Salp Swarm Optimization Algorithm (MSSA), which mathematically models and mimics the behavior of salp swarms for solving multiple objective optimization problems. Under this model framework, several conditional quantiles of wind power can be estimated and predicted at the same time. Moreover, due to the joint estimation process, the problem of quantile curve crossing can be effectively weakened during the process of model training. The qualitative and quantitative performances of the proposed method are tested and verified on a set of real case studies. The results show that the proposed algorithm provides superior outputs compared to the well-known and recent algorithms in the literature. Highlights: The proposed model can provide multiple quantile predictions simultaneously. It is the first time to construct composite kernels for the joint quantile model. This paper considers the autocorrelation of the transformed input and output. This study uses a new meta-heuristics algorithm to optimize the proposed model.