۱- Introduction

۲- Proposed methodology

۳- Architecture of hybrid wind power forecasting model

۴- Cases study and analysis

۵- Conclusion

References

Abstract

Given the intermittency and randomness of wind energy, the mass grid connection of wind power poses great challenges in power system and increases the threat in power system balance. Wind power forecasting can predict the fluctuation of output wind power in wind farms, which can effectively reduce wind power uncertainty. Improving the accuracy of wind power is indispensable for enhancing the efficiency of wind power utilization. To improve the forecasting accuracy, this research proposed a novel wind power forecasting method based on singular spectrum analysis and a new hybrid Laguerre neural network. First, singular spectrum analysis was used to analyze the wind power series, which decomposes the series into two subsequences, namely, trend and harmonic series and noise series. Then, Laguerre neural network and new Laguerre neural network were proposed to build the hybrid forecasting model optimized by the opposition transition state transition algorithm. The two decomposed signals were used for forecasting the future wind power value by using a forecasting model. Finally, the proposed hybrid forecasting method was investigated with respect to the wind farm in Xinjiang, China. Prediction performance results demonstrated that the proposed model has higher accuracy than the Laguerre neural network, hybrid Laguerre neural network, hybrid Laguerre neural network with singular spectrum analysis, hybrid Laguerre neural network with opposition transition state transition algorithm and singular spectrum analysis, and other popular methods.

Introduction

Efficient, clean, and low carbon has become the mainstream of the world’s energy development direction. Wind energy, as one of the most popular renewable clean energies, has developed rapidly in recent years. The total installed capacity of 14 years (from 2005 year to 2018 year) is reported in Fig. 1. It can be seen from Fig. 1 that the wind power installed capacity maintains sustained growth state in recent years, indicating that wind energy utilization remains rapidly increasing. The overall capacity of all wind turbines installed worldwide by the end of 2018 reached 597 GW [1]. Given the intermittency and randomness of wind energy, the mass grid connection of wind power poses great challenges in power system and increases the threat in power system balance. Wind power forecasting is a technology for predicting the fluctuation of output wind power in wind farms, which can effectively reduce wind power uncertainty. In other words, the accuracy of wind power forecasting is the basis of wind system optimization scheduling and efficient accommodation [2]. Improving the forecasting accuracy of wind power is indispensable for enhancing the efficiency of wind power utilization [3].