Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms
Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/7/2602 |
| _version_ | 1797439395501965312 |
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| author | Saeed Salah Husain R. Alsamamra Jawad H. Shoqeir |
| author_facet | Saeed Salah Husain R. Alsamamra Jawad H. Shoqeir |
| author_sort | Saeed Salah |
| collection | DOAJ |
| description | Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (<i>R</i><sup>2</sup>). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site. |
| first_indexed | 2024-03-09T11:53:13Z |
| format | Article |
| id | doaj.art-35d7a5ae8b2247109ff40651324327df |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T11:53:13Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-35d7a5ae8b2247109ff40651324327df2023-11-30T23:12:30ZengMDPI AGEnergies1996-10732022-04-01157260210.3390/en15072602Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning AlgorithmsSaeed Salah0Husain R. Alsamamra1Jawad H. Shoqeir2Department of Computer Science, Al-Quds University, P.O. Box 89, Abu-Dies, Jerusalem 20002, PalestineDepartment of Physics, Al-Quds University, P.O. Box 89, Abu-Dies, Jerusalem 20002, PalestineDepartment of Earth and Environmental Sciences, Al-Quds University, P.O. Box 89, Abu-Dies, Jerusalem 20002, PalestineWind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (<i>R</i><sup>2</sup>). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site.https://www.mdpi.com/1996-1073/15/7/2602wind speedwind energymachine-learning algorithmsartificial neural networkmean absolute percentage error |
| spellingShingle | Saeed Salah Husain R. Alsamamra Jawad H. Shoqeir Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms Energies wind speed wind energy machine-learning algorithms artificial neural network mean absolute percentage error |
| title | Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms |
| title_full | Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms |
| title_fullStr | Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms |
| title_full_unstemmed | Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms |
| title_short | Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms |
| title_sort | exploring wind speed for energy considerations in eastern jerusalem palestine using machine learning algorithms |
| topic | wind speed wind energy machine-learning algorithms artificial neural network mean absolute percentage error |
| url | https://www.mdpi.com/1996-1073/15/7/2602 |
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