短期电力负荷预测用SVR实现LibSVM包和Python代码

Manoj Baghel, Abir Ghosh, N. Singh, Ashutosh Kumar Singh
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引用次数: 2

摘要

电力负荷预测是电力市场中的一个重要课题,它已经由机器学习方法:支持向量机(SVM)来完成。支持向量机负荷预测与影响负荷的参数形成非线性关系;此外,在周末和节假日负荷曲线的正确建模。过去的信息被用作应用的样本,因此假期相关需求作为预测的重要因素。LibSVM包和Python代码用于对SVM进行建模。并对两种方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Short - term electric load forecasting using SVR implementing LibSVM package and Python code
Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear relations with the parameters that have an effect on the load; additionally to the correct modeling of the load curve on weekends and holidays. The past information is used as a sample for the applying and therefore holidays associated demand as an important factor inprediction. The LibSVM package and Python codeis used for modeling the SVM. Resultsare obtainedand comparison is made for the two methods.
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