A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence
Wind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, st...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/8/4/420 |
| _version_ | 1798035034924384256 |
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| author | Ioannis P. Panapakidis Constantine Michailides Demos C. Angelides |
| author_facet | Ioannis P. Panapakidis Constantine Michailides Demos C. Angelides |
| author_sort | Ioannis P. Panapakidis |
| collection | DOAJ |
| description | Wind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble„ refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions. |
| first_indexed | 2024-04-11T20:52:37Z |
| format | Article |
| id | doaj.art-3477c9b55d524654982f97b5668285cf |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T20:52:37Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-3477c9b55d524654982f97b5668285cf2022-12-22T04:03:47ZengMDPI AGElectronics2079-92922019-04-018442010.3390/electronics8040420electronics8040420A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational IntelligenceIoannis P. Panapakidis0Constantine Michailides1Demos C. Angelides2Department of Electrical Engineering, Technological Educational Institute of Thessaly, 41110 Larisa, GreeceDepartment of Civil Engineering and Geomatics, Cyprus University of Technology, 3036 Limassol, CyprusDepartment of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceWind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble„ refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions.https://www.mdpi.com/2079-9292/8/4/420computational intelligenceoffshore windforecastingmachine learningneural networksneuro-fuzzy systems |
| spellingShingle | Ioannis P. Panapakidis Constantine Michailides Demos C. Angelides A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence Electronics computational intelligence offshore wind forecasting machine learning neural networks neuro-fuzzy systems |
| title | A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence |
| title_full | A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence |
| title_fullStr | A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence |
| title_full_unstemmed | A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence |
| title_short | A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence |
| title_sort | data driven short term forecasting model for offshore wind speed prediction based on computational intelligence |
| topic | computational intelligence offshore wind forecasting machine learning neural networks neuro-fuzzy systems |
| url | https://www.mdpi.com/2079-9292/8/4/420 |
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