Exploring Time Series Models for Wind Speed Forecasting: A Comparative Analysis

Xiangqian Li (), Keke Li, Siqi Shen and Yaxin Tian
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Xiangqian Li: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Keke Li: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Siqi Shen: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Yaxin Tian: School of Finance, Capital University of Economics and Business, Beijing 100070, China

Energies, 2023, vol. 16, issue 23, 1-22

Abstract: The sustainability and efficiency of the wind energy industry rely significantly on the accuracy and reliability of wind speed forecasting, a crucial concern for optimal planning and operation of wind power generation. In this study, we comprehensively evaluate the performance of eight wind speed prediction models, spanning statistical, traditional machine learning, and deep learning methods, to provide insights into the field of wind energy forecasting. These models include statistical models such as ARIMA (AutoRegressive Integrated Moving Average) and GM (Grey Model), traditional machine learning models like LR (Linear Regression), RF (random forest), and SVR (Support Vector Regression), as well as deep learning models comprising ANN (Artificial Neural Network), LSTM (Long Short-Term Memory), and CNN (Convolutional Neural Network). Utilizing five common model evaluation metrics, we derive valuable conclusions regarding their effectiveness. Our findings highlight the exceptional performance of deep learning models, particularly the Convolutional Neural Network (CNN) model, in wind speed prediction. The CNN model stands out for its remarkable accuracy and stability, achieving the lowest mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the higher coefficient of determination ( R 2 ). This underscores the CNN model’s outstanding capability to capture complex wind speed patterns, thereby enhancing the sustainability and reliability of the renewable energy industry. Furthermore, we emphasized the impact of model parameter tuning and external factors, highlighting their potential to further improve wind speed prediction accuracy. These findings hold significant implications for the future development of the wind energy industry.

Keywords: wind speed; machine learning; deep learning; time series prediction; convolutional neural network (CNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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