Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network

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 tim...

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Autores principales:	Abdulmajid Lawal, Shafiqur Rehman, Luai M. Alhems, Md. Mahbub Alam
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Convolutional neural network long short term memory wind speed prediction machine learning hybrid model above ground level Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/6eb8a4e5698c49dfa79c1623bcb788f7
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id	oai:doaj.org-article:6eb8a4e5698c49dfa79c1623bcb788f7
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spelling	oai:doaj.org-article:6eb8a4e5698c49dfa79c1623bcb788f72021-12-02T00:00:55ZWind Speed Prediction Using Hybrid 1D CNN and BLSTM Network2169-353610.1109/ACCESS.2021.3129883https://doaj.org/article/6eb8a4e5698c49dfa79c1623bcb788f72021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9623538/https://doaj.org/toc/2169-3536As 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.Abdulmajid LawalShafiqur RehmanLuai M. AlhemsMd. Mahbub AlamIEEEarticleConvolutional neural networklong short term memorywind speed predictionmachine learninghybrid modelabove ground levelElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156672-156679 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Convolutional neural network long short term memory wind speed prediction machine learning hybrid model above ground level Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Convolutional neural network long short term memory wind speed prediction machine learning hybrid model above ground level Electrical engineering. Electronics. Nuclear engineering TK1-9971 Abdulmajid Lawal Shafiqur Rehman Luai M. Alhems Md. Mahbub Alam Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network
description	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.
format	article
author	Abdulmajid Lawal Shafiqur Rehman Luai M. Alhems Md. Mahbub Alam
author_facet	Abdulmajid Lawal Shafiqur Rehman Luai M. Alhems Md. Mahbub Alam
author_sort	Abdulmajid Lawal
title	Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network
title_short	Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network
title_full	Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network
title_fullStr	Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network
title_full_unstemmed	Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network
title_sort	wind speed prediction using hybrid 1d cnn and blstm network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/6eb8a4e5698c49dfa79c1623bcb788f7
work_keys_str_mv	AT abdulmajidlawal windspeedpredictionusinghybrid1dcnnandblstmnetwork AT shafiqurrehman windspeedpredictionusinghybrid1dcnnandblstmnetwork AT luaimalhems windspeedpredictionusinghybrid1dcnnandblstmnetwork AT mdmahbubalam windspeedpredictionusinghybrid1dcnnandblstmnetwork
_version_	1718403990977249280

Wind Speed Prediction Using Hybrid 1D CNN and BLSTM Network

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