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A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data
Energy Reports ( IF 5.2 ) Pub Date : 2021-12-24 , DOI: 10.1016/j.egyr.2021.10.102
Boyu He 1 , Lin Ye 1 , Ming Pei 1 , Peng Lu 1 , Binhua Dai 1 , Zhuo Li 1 , Kaifeng Wang 1
Affiliation  

Due to the fluctuation and intermittency of wind power resources, large-scale wind power integration brings serious challenges to power systems. Among the existing short-term forecasting methods, the accuracy and forecast period length can hardly meet the demand of power system economic dispatching and day-ahead power purchase markets. To further enhance the accuracy and increase the time scale, a short-term wind power forecasting (WPF) combined model based on numerical weather prediction (NWP) analysis is presented in this paper. First, according to the criterion of the minimum redundancy maximum relevance (mRMR) algorithm, several factors are sifted from the NWP multivariate data. Second, different characteristics of the factors are extracted, and weather patterns are divided into different types on the basis of these characteristics. Third, two deep learning models, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), are applied for short-term WPF respectively under different weather types. Furthermore, the forecast results of the two models are combined using the Induced Ordered Weighted Average (IOWA) operator. The actual data collected from a wind farm in Northwest China are used to verify the conclusions. Results show that the proposed method can forecast wind power under different weather circumstances and outperform existing Radial Basis Function (RBF), Extreme Learning Machine (ELM) and Support Vector Machine (SVM)-based methods with respect to forecasting accuracy.

中文翻译:

基于数值天气预报数据分析的短期风电功率预测组合模型

由于风电资源的波动性和间歇性,大规模风电并网给电力系统带来严峻挑战。现有的短期预测方法中,其准确度和预测周期长度很难满足电力系统经济调度和日前购电市场的需求。为了进一步提高预测精度和增加时间尺度,本文提出了一种基于数值天气预报(NWP)分析的短期风电预测(WPF)组合模型。首先,根据最小冗余最大相关性(mRMR)算法的准则,从数值天气预报多元数据中筛选出几个因子。其次,提取因素的不同特征,并根据这些特征将天气模式划分为不同类型。第三,卷积神经网络(CNN)和长短期记忆(LSTM)两种深度学习模型分别应用于不同天气类型下的短期WPF。此外,使用诱导有序加权平均 (IOWA) 算子组合两个模型的预测结果。利用西北地区某风电场的实际数据验证了结论。结果表明,该方法可以预测不同天气情况下的风电功率,并且在预测精度方面优于现有的基于径向基函数(RBF)、极限学习机(ELM)和支持向量机(SVM)的方法。
更新日期:2021-12-24
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