Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm

Jie Hao,Changsheng Zhu,Xiuting Guo

doi:10.1109/access.2020.3048382

Jie Hao, Changsheng Zhu + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3048382

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Abstract

Under the background of big data, the use of massive online data to improve the real-time characteristics and reliability of wind power prediction and to reduce the impact of wind farms on the power grid makes the power supply and demand balance important problems to solve. This paper provides a new solution for short-term wind power forecasting to address these problems. In this paper, an improved random forest short-term prediction model based on the hierarchical output power is proposed, and it is used to forecast the power output of a real wind farm located in Northwest China. First, a chi-square test is adopted to discretize the power data to divide the large-scale training data and remove abnormal data. The novelty of this study is the establishment of a classification model with the output wind power as the classification target and the use of Poisson re-sampling to replace the bootstrap method of the random forest, that is, to improve the training speed of the random forest algorithm. The results indicate that the proposed technique can estimate the output wind power with an MSE of 0.0232, and the comparison illustrates the effectiveness and superiority of the proposed method.

Highlights

Wind energy is a renewable and clean energy source with large storage capacity and wide distribution
Wind power prediction technology is an effective method to mitigate the negative effects of wind power grid connections [3]
This paper proposes a hierarchical wind power prediction model, and the larger data set is divided into smaller training sets for modelling

Summary

Introduction

Wind energy is a renewable and clean energy source with large storage capacity and wide distribution. Wind power has become the fastest growing renewable energy power generation technology in the world [1], [2]. Wind power is volatile and intermittent, and large-scale wind power grid connections represent severe challenges to the safe and stable operation of power systems. Wind power prediction technology is an effective method to mitigate the negative effects of wind power grid connections [3]. Accurate and reliable wind power prediction has greatly contributed to dynamic economic dispatch in power systems. The wind power permeability is increased, the rotating spare capacity is reduced, and the wind farm capacity coefficient is stabilized [4]

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