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首页> 外文期刊>Energies >A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting
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A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

机译：基于极限学习机，k-最近邻回归和小波降噪的新型混合模型在短期电力负荷预测中的应用

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Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM), which combines k-Nearest Neighbor (KNN) and Extreme Learning Machine (ELM) based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM), Wavelet Denoising-Extreme Learning Machine (WKM) and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM), the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.
机译：电力负荷预测在电力市场和电力系统中起着重要作用。由于电负载时间序列复杂且非线性，因此很难获得令人满意的预测精度。本文提出了一种基于k-最近邻回归（EWKM）优化的混合模型小波去噪-极限学习机，该模型结合了基于小波去噪技术的k-最近邻（KNN）和极限学习机（ELM）。短期负荷预测。提出的混合模型首先将时间序列分解为与低频相关的主信号和与高频相关的一些详细信号，然后使用KNN从低频信号中确定自变量和因变量。最后，使用ELM来获取这些变量之间的非线性关系，以获得电负载的最终预测结果。与其他三个模型相比，该模型通过k最近邻回归（EKM）优化过的极限学习机，小波消噪极限学习机（WKM）和通过k最近邻回归（WNNM）优化过的小波消噪反向传播神经网络本文提出的方法可以有效地提高精度。新南威尔士州是澳大利亚的经济重镇，因此我们使用提议的模型来预测该地区的电力需求。准确的预测具有重要意义。

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