利用机器学习预测短期风能发电

Noman Shabbir, Roya Ahmadiahangar, L. Kütt, M. N. Iqbal, A. Rosin
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引用次数: 20

摘要

由于风能的不稳定性和随机性,它与其他能源非常不同。风力发电预测是解决电力系统可靠性问题的一个重要方面,也是解决电力系统供需平衡问题的一个挑战。本文采用基于支持向量机(SVM)的回归算法对爱沙尼亚风电产量进行了一天前预测。然后将提出的算法与爱沙尼亚能源监管组织使用的预测算法的结果进行比较。结果表明,我们提出的算法具有较好的预测效果和最低的均方根误差(RMSE)值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Short Term Wind Energy Generation using Machine Learning
Wind power is very different from other sources due to its volatile and stochastic nature. The forecasting of wind energy generation is a very important aspect for the reliability and challenges regarding balancing the supply and demand in power systems. In this paper, Support Vector Machine (SVM) based regression algorithm is used for one day ahead prediction of wind energy production in Estonia. The proposed algorithm is then compared with the results of the prediction algorithm used by the Estonian energy regulatory organization. The results indicate that our proposed algorithms give better forecasting and the lowest Root Mean Square Error (RMSE) values.
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