A New Convolutional Neural Networks and LSTM Based model for Realtime Wind Forecasting

Tahmasb Baghini, Zahra

doi:10.48306/estm.2026.595855.1020

A New Convolutional Neural Networks and LSTM Based model for Realtime Wind Forecasting

Document Type : Original Article

Author

Zahra Tahmasb Baghini

Department of Computer Engineering, Kerman branch, Islamic Azad University, Kerman, Iran.

10.48306/estm.2026.595855.1020

Abstract

Nowadays, renewable energy resources like wind energy generation are the most important and critical issues for fossil fuel replacement and environmental rescue. Although recent wind power generation has developed rapidly, the planning of these generations is still difficult because the accurate forecasting of wind speed is challenging due to its volatile and uncertain nature, particularly in coastal areas. Therefore, this study proposes a real-time wind speed forecasting model based on a hybrid convolutional neural network architecture, where multiple meteorological variables are filtered through a cross-screening strategy for multivariate time series forecasting. To screen the parameters for training, an evaluation was conducted based on multi-feature wind speed datasets. The performances of the proposed and baseline models in terms of real-time wind speed forecasting accuracy and volatility were evaluated based on a comparative study of error metrics. To benchmark the results, the proposed model was compared with other state-of-the-art paradigms based on error metrics. The results indicate that the proposed CNN-LSTM model outperformed all baseline models in terms of training efficiency and delivered values with higher accuracy than the other models. Based on different test datasets, the CNN-LSTM model with the same hyperparameter configurations predicted more accurately than other baseline models under the collected datasets. For 10-meter datasets the RMSE index obtained as 0.25, 0.42 and 0.25. Also, for 50-meter datasets the RMSE index obtained as 0.26, 0.51 and 0.25. These results prove the proposed model accuracy.

Keywords

Wind speed forecasting

Convolutional

Neural Networks

Long Short-Term Memory

Feature Selection

Realtime forecasting

Subjects

Energy Conversion and Management

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