Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network
| العنوان: | Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network |
|---|---|
| المؤلفون: | Luai M. Alhems, Abdulmajid Lawal, Md. Mahbub Alam, Shafiqur Rehman |
| المصدر: | IEEE Access, Vol 9, Pp 156672-156679 (2021) |
| بيانات النشر: | IEEE, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | General Computer Science, Mean squared error, Computer science, long short term memory, General Engineering, Stability (learning theory), hybrid model, Convolutional neural network, Wind speed, Term (time), wind speed prediction, TK1-9971, Mean absolute percentage error, machine learning, Benchmark (computing), General Materials Science, Electrical engineering. Electronics. Nuclear engineering, Time series, Algorithm, above ground level |
| الوصف: | As the world witnesses population increase, the global power demand is increasing and the need for exploring other alternative clean and self-renewable sources of energy such as wind has become necessary. For optimal operation of the wind farms and stability of the grid, wind prediction ahead of time is of key importance. An accurate forecast of wind speed is often difficult due to the unpredictable nature of the wind. In this work, we utilized different machine learning models and proposed a hybrid machine learning approach. This approach combines 1D convolutional neural network (CNN) and bidirectional long short term memory (BLSTM) network for accurate prediction of short term wind prediction at different heights above ground level (AGL). The 1D CNN model extracts high-level features of the input wind speed data. The extracted features are then fed as input to the BLSTM network for wind speed prediction. The wind speed time series data used in this study are measured at 18, and 98 meters AGL. The study further presents a relationship between the utilized models and prediction accuracy at different heights. The forecasting performance of the models tends to increase as the height AGL increases. A real-world case study is implemented to demonstrate the effectiveness of the proposed CNN-BLSTM method in Saudi Arabia. The mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used as performance indices to evaluate the performance of the proposed CNN-BLTSM model. The corresponding results show that the proposed method outperforms other benchmark models. |
| اللغة: | English |
| تدمد: | 2169-3536 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0db7d4256545b69a6a56de839dc5478f https://ieeexplore.ieee.org/document/9623538/ |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....0db7d4256545b69a6a56de839dc5478f |
| قاعدة البيانات: | OpenAIRE |
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