Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting
Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting
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DOI：
10.1016/j.measurement.2017.02.007
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发表时间：
2017-06-01
期刊：
影响因子：
5.6
通讯作者：
Bican, Bahadir
中科院分区：
文献类型：
--
作者：
Yaslan, Yusuf;Bican, Bahadir
电力负荷需求预测对电力行业和经济周期的经济政策有着重要的影响。预测负荷需求的变化可以了解未来的趋势，并可以在战略、规划或投资方面做出明确的决策。此外，能够了解未来价值的波动有助于日常/每周和长期运营的管理。采用经验模式分解(EMD)和支持向量机回归(SVR)相结合的混合方法对电力负荷需求进行预测。提出的EMD-SVR方法将EMD方法与支持向量机算法相结合，将EMD作为训练数据的去噪步骤。与以往的研究不同，该算法不依赖于特定的本征模式函数(IMF)来进行去噪和模型学习。在3个不同国家的电力负荷数据上进行了实验，并与采用不同特征向量的支持向量机算法进行了比较。仿真结果表明，该算法在电力负荷预测中的性能优于SVR算法和无特征去噪SVR算法。(C)2017爱思唯尔有限公司。保留所有权利。
Electricity load demand estimation has a remarkable impact on the economic policies of power industry and business cycles. Forecasting the movements of load demand provides to know the tendency of the future and can lead to a clear decision in strategic, planning or investments. Besides the ability to know the fluctuations of the future values contributes to the management of daily/weekly and long term operations. This study aims to predict the electricity load demand using a hybrid method that incorporates Empirical Mode Decomposition (EMD) and Support Vector Regression (SVR) algorithms. The proposed EMD-SVR method integrates the EMD method to SVR algorithm by using EMD as a denoising step on the training data. Unlike the previous studies, the proposed algorithm is not dependent to a specific Intrinsic Mode Function (IMF) for denoising and model learning. Experimental results are conducted on 3 electricity load datasets from different countries and the proposed method is compared with SVR algorithm using different feature vectors as well. It is shown that the proposed algorithm outperforms the SVR and non-feature used denoised-SVR algorithm on electricity load forecasting. (C) 2017 Elsevier Ltd. All rights reserved.