ARIMA vs. Neural networks for wind speed forecasting

J. Palomares-Salas, J. D. L. de la Rosa, J. Ramiro, J. Melgar, A. Aguera, A. Moreno
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引用次数: 81

Abstract

In this paper an ARIMA model is used for time-series forecast involving wind speed measurements. Results are compared with the performance of a back propagation type NNT. Results show that ARIMA model is better than NNT for short time-intervals to forecast (10 minutes, 1 hour, 2 hours and 4 hours). Data was acquired from a unit located in Southern Andalusia (Peñaflor, Sevilla), with a soft orography (10 minutes between measurements). This feature is which makes performance of the ARIMA model and the NNT very similar, so a simple forecasting model could be used in order to administrate energy sources. The paper presents the process of model validation, along with a regression analysis, based in real-life data.
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ARIMA与神经网络风速预报
本文将ARIMA模型用于风速测量的时间序列预报。结果与反向传播型NNT的性能进行了比较。结果表明,ARIMA模型在短时间间隔(10分钟、1小时、2小时和4小时)的预报效果优于NNT模型。数据来自位于安达卢西亚南部(Peñaflor,塞维利亚)的一个装置,具有软地形(测量间隔10分钟)。这一特征使得ARIMA模型和NNT的性能非常相似,因此可以使用一个简单的预测模型来管理能源。本文介绍了模型验证的过程,以及基于实际数据的回归分析。
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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