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首页> 外文期刊>Journal of Power and Energy Engineering >Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting
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Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting

机译：经验模式分解-k最近邻模型用于风速预测

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摘要

Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-kNN-P that applies kNN on each decomposed intrinsic mode function (IMF) and residue for separate modelling and forecasting followed by summation and an EMD-kNN-M that forms a feature vector set from all IMFs and residue followed by a single kNN modelling and forecasting. These two configurations are compared with the persistent model and the conventional kNN model on a wind speed time series dataset from Singapore. The results show that the two EMD-kNN hybrid models have good performance for longer term forecasting and EMD-kNN-M has better performance than EMD-kNN-P for shorter term forecasting.
机译：混合模型是可再生能源相关预测应用中流行的预测模型。风速预测作为一种常见应用，需要快速而准确的预测模型。本文介绍了一种经验模式分解（EMD），然后是一个k最近邻（kNN）混合模型进行风速预测。详细讨论了EMD-kNN的两种配置：EMD-kNN-P，它将kNN应用于每个分解的固有模式函数（IMF）和残差，以进行单独的建模和预测，然后进行求和和形成特征的EMD-kNN-M所有IMF和残差的向量集，然后进行单个kNN建模和预测。在新加坡的风速时间序列数据集上，将这两种配置与持久性模型和常规kNN模型进行了比较。结果表明，两种EMD-kNN混合模型在长期预测中均具有良好的性能，而EMD-kNN-M在短期预测中的性能优于EMD-kNN-P。

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