基于统计和深度学习模型的风速预测

IF 2.4 Q3 ENERGY & FUELS
Ilham Tyass, Tajeddine Khalili, Mohamed Rafik, Bellat Abdelouahed, A. Raihani, K. Mansouri
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引用次数: 4

摘要

风能是可再生能源的主要来源,具有很高的可持续发展潜力。然而,风能的间歇性和不稳定性影响了风能转换系统的效率和可靠性。可用风势的预测也因其不稳定性质而存在严重缺陷。因此,通过风速预测评估风能对于使能源生产适应负荷变化和用户需求率至关重要。本工作旨在使用统计的季节自回归综合移动平均(SARIMA)模型和长短期记忆的深度神经网络(LSTM)模型来预测风速。为了阐明这些方法,进行了比较分析,以选择最合适的风速预测模型。误差度量、均方误差(MSE)、均方根误差(RMSE)、平均绝对误差(MAE)和平均绝对百分比误差(MAPE)用于评估每个模型的有效性,并用于选择最佳预测模型。总体而言,所获得的结果表明,与SARIMA相比,LSTM模型显示出领先的性能,平均绝对百分比误差(MAPE)为14.05%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind Speed Prediction Based on Statistical and Deep Learning Models
Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%.
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来源期刊
CiteScore
4.50
自引率
16.00%
发文量
83
审稿时长
8 weeks
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