Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks
The rapid growth of wind and solar energy penetration has created critical issues, such as fluctuation, uncertainty, and intermittence, that influence the power system stability, grid operation, and the balance of the power supply. Improving the reliability and accuracy of wind and solar energy pred...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/20/6501 |
| _version_ | 1797514762653794304 |
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| author | Fahad Radhi Alharbi Denes Csala |
| author_facet | Fahad Radhi Alharbi Denes Csala |
| author_sort | Fahad Radhi Alharbi |
| collection | DOAJ |
| description | The rapid growth of wind and solar energy penetration has created critical issues, such as fluctuation, uncertainty, and intermittence, that influence the power system stability, grid operation, and the balance of the power supply. Improving the reliability and accuracy of wind and solar energy predictions can enhance the power system stability. This study aims to contribute to the issues of wind and solar energy fluctuation and intermittence by proposing a high-quality prediction model based on neural networks (NNs). The most efficient technology for analyzing the future performance of wind speed and solar irradiance is recurrent neural networks (RNNs). Bidirectional RNNs (BRNNs) have the advantages of manipulating the information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BRNN’s output layer concurrently receives information from both the backward layers and the forward layers. The bidirectional long short-term memory (BI-LSTM) prediction model was designed to predict wind speed, solar irradiance, and ambient temperature for the next 169 h. The solar irradiance data include global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The historical data collected from Dumat al-Jandal City covers the period from 1 January 1985 to 26 June 2021, as hourly intervals. The findings demonstrate that the BI-LSTM model has promising performance in terms of evaluation, with considerable accuracy for all five types of historical data, particularly for wind speed and ambient temperature values. The model can handle different sizes of sequential data and generates low error metrics. |
| first_indexed | 2024-03-10T06:36:05Z |
| format | Article |
| id | doaj.art-dd14f4f2ac004c18a5c100f857b68a71 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:36:05Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-dd14f4f2ac004c18a5c100f857b68a712023-11-22T18:04:10ZengMDPI AGEnergies1996-10732021-10-011420650110.3390/en14206501Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural NetworksFahad Radhi Alharbi0Denes Csala1Engineering Department, Lancaster University, Lancaster LA1 4YR, UKEngineering Department, Lancaster University, Lancaster LA1 4YR, UKThe rapid growth of wind and solar energy penetration has created critical issues, such as fluctuation, uncertainty, and intermittence, that influence the power system stability, grid operation, and the balance of the power supply. Improving the reliability and accuracy of wind and solar energy predictions can enhance the power system stability. This study aims to contribute to the issues of wind and solar energy fluctuation and intermittence by proposing a high-quality prediction model based on neural networks (NNs). The most efficient technology for analyzing the future performance of wind speed and solar irradiance is recurrent neural networks (RNNs). Bidirectional RNNs (BRNNs) have the advantages of manipulating the information in two opposing directions and providing feedback to the same outputs via two different hidden layers. A BRNN’s output layer concurrently receives information from both the backward layers and the forward layers. The bidirectional long short-term memory (BI-LSTM) prediction model was designed to predict wind speed, solar irradiance, and ambient temperature for the next 169 h. The solar irradiance data include global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI). The historical data collected from Dumat al-Jandal City covers the period from 1 January 1985 to 26 June 2021, as hourly intervals. The findings demonstrate that the BI-LSTM model has promising performance in terms of evaluation, with considerable accuracy for all five types of historical data, particularly for wind speed and ambient temperature values. The model can handle different sizes of sequential data and generates low error metrics.https://www.mdpi.com/1996-1073/14/20/6501bidirectionalneural networksBI-LSTM modelpredictionsolar irradiancewind speed |
| spellingShingle | Fahad Radhi Alharbi Denes Csala Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks Energies bidirectional neural networks BI-LSTM model prediction solar irradiance wind speed |
| title | Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks |
| title_full | Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks |
| title_fullStr | Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks |
| title_full_unstemmed | Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks |
| title_short | Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks |
| title_sort | wind speed and solar irradiance prediction using a bidirectional long short term memory model based on neural networks |
| topic | bidirectional neural networks BI-LSTM model prediction solar irradiance wind speed |
| url | https://www.mdpi.com/1996-1073/14/20/6501 |
| work_keys_str_mv | AT fahadradhialharbi windspeedandsolarirradiancepredictionusingabidirectionallongshorttermmemorymodelbasedonneuralnetworks AT denescsala windspeedandsolarirradiancepredictionusingabidirectionallongshorttermmemorymodelbasedonneuralnetworks |