Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization

Jianguo Zhou, Xuechao Yu and Baoling Jin
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Jianguo Zhou: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China
Xuechao Yu: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China
Baoling Jin: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China

Sustainability, 2018, vol. 10, issue 9, 1-18

Abstract: The nonlinear and non-stationary nature of wind power creates a difficult challenge for the stable operation of the power system when it accesses the grid. Improving the prediction accuracy of short-term wind power is beneficial to the power system dispatching department in formulating a power generation plan, reducing the rotation reserve capacity and improving the safety and reliability of the power grid operation. This paper has constructed a new hybrid model, named the ESMD-PSO-ELM model, which combines Extreme-point symmetric mode decomposition (ESMD), Extreme Learning Machine (ELM) and Particle swarm optimization (PSO). Firstly, the ESMD is applied to decompose wind power into several intrinsic mode functions (IMFs) and one residual(R). Then, the PSO-ELM is applied to predict each IMF and R. Finally, the predicted values of these components are assembled into the final forecast value compared with the original wind power. To verify the predictive performance of the proposed model, this paper selects actual wind power data from 1 April 2016 to 30 April 2016 with a total of 2880 observation values located in Yunnan, China for the experimental sample. The MAPE, N MAE and N RMSE values of the proposed model are 4.76, 2.23 and 2.70, respectively, and these values are lower than those of the other eight models. The empirical study demonstrates that the proposed model is more robust and accurate in forecasting short-term wind power compared with the other eight models.

Keywords: wind power; hybrid model; extreme-point symmetric mode decomposition; extreme learning machine; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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