Evaluation of XGBoost vs. other Machine Learning models for wind parameters identification

Q4 Energy
B. García-Puente, A. Rodríguez-Hurtado, M. Santos, J. Sierra-García
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引用次数: 0

Abstract

Wind energy is one of the most promising renewable energies. But wind is a quite unstable resource due to its continuous variation and random nature. This uncertainty affects the production cost. Therefore, accurate forecasting of wind and energy is very interesting for energy markets. In this work, we test a recent and powerful intelligent technique, extreme gradient boosting (XGBoost), for wind prediction. The forecasting models of some wind features with XGBoost are compared with Support Vector Regression (SVR), Gaussian Process Regression (GPR) and Neural Networks (NN) models. Specifically, the three features predicted are the active power generated by the turbine, the wind speed, and the wind direction. The results conclude that these techniques are useful for wind and energy forecasting, with XGBoost being the most outstanding one, especially for short-term predictions.
XGBoost与其他机器学习模型在风参数识别中的比较
风能是最有前途的可再生能源之一。但由于风的连续变化和随机性,它是一种非常不稳定的资源。这种不确定性影响了生产成本。因此,对风能和能源的准确预测对能源市场来说是非常有趣的。在这项工作中,我们测试了一种最新的强大智能技术,极端梯度增强(XGBoost),用于风的预测。将XGBoost模型与支持向量回归(SVR)、高斯过程回归(GPR)和神经网络(NN)模型进行了比较。具体来说,预测的三个特征是涡轮机产生的有功功率、风速和风向。结果表明,这些技术对风能和能源预测是有用的,其中XGBoost是最突出的,特别是对于短期预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Renewable Energy and Power Quality Journal
Renewable Energy and Power Quality Journal Energy-Energy Engineering and Power Technology
CiteScore
0.70
自引率
0.00%
发文量
147
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