Wind Speed Forecasting with a Clustering-Based Deep Learning Model
The predictability of wind energy is crucial due to the uncertain and intermittent features of wind energy. This study proposes wind speed forecasting models, which employ time series clustering approaches and deep learning methods. The deep learning (LSTM) model utilizes the preprocessed data as in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/24/13031 |
| _version_ | 1827641907055951872 |
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| author | Fuat Kosanoglu |
| author_facet | Fuat Kosanoglu |
| author_sort | Fuat Kosanoglu |
| collection | DOAJ |
| description | The predictability of wind energy is crucial due to the uncertain and intermittent features of wind energy. This study proposes wind speed forecasting models, which employ time series clustering approaches and deep learning methods. The deep learning (LSTM) model utilizes the preprocessed data as input and returns data features. The Dirichlet mixture model and dynamic time-warping method cluster the time-series data features and then deep learning in forecasting. Particularly, the Dirichlet mixture model and dynamic warping method cluster the time-series data features. Next, the deep learning models use the entire (global) and clustered (local) data to capture the long-term and short-term patterns, respectively. Furthermore, an ensemble model is obtained by integrating the global model and local model results to exploit the advantages of both models. Our models are tested on four different wind data obtained from locations in Turkey with different wind regimes and geographical aspects. The numerical results indicate that the proposed ensemble models achieve the best accuracy compared to the deep learning method (LSTM). The results imply that the feature clustering approach accommodates a promising framework in forecasting. |
| first_indexed | 2024-03-09T17:21:16Z |
| format | Article |
| id | doaj.art-92ac557e1ba54b7b8de1464e8fe68072 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:21:16Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-92ac557e1ba54b7b8de1464e8fe680722023-11-24T13:08:58ZengMDPI AGApplied Sciences2076-34172022-12-0112241303110.3390/app122413031Wind Speed Forecasting with a Clustering-Based Deep Learning ModelFuat Kosanoglu0Department of Industrial Engineering, Yalova University, Yalova 77200, TurkeyThe predictability of wind energy is crucial due to the uncertain and intermittent features of wind energy. This study proposes wind speed forecasting models, which employ time series clustering approaches and deep learning methods. The deep learning (LSTM) model utilizes the preprocessed data as input and returns data features. The Dirichlet mixture model and dynamic time-warping method cluster the time-series data features and then deep learning in forecasting. Particularly, the Dirichlet mixture model and dynamic warping method cluster the time-series data features. Next, the deep learning models use the entire (global) and clustered (local) data to capture the long-term and short-term patterns, respectively. Furthermore, an ensemble model is obtained by integrating the global model and local model results to exploit the advantages of both models. Our models are tested on four different wind data obtained from locations in Turkey with different wind regimes and geographical aspects. The numerical results indicate that the proposed ensemble models achieve the best accuracy compared to the deep learning method (LSTM). The results imply that the feature clustering approach accommodates a promising framework in forecasting.https://www.mdpi.com/2076-3417/12/24/13031wind speed forecastingDirichlet mixture modeldynamic time warpingclusteringLSTM |
| spellingShingle | Fuat Kosanoglu Wind Speed Forecasting with a Clustering-Based Deep Learning Model Applied Sciences wind speed forecasting Dirichlet mixture model dynamic time warping clustering LSTM |
| title | Wind Speed Forecasting with a Clustering-Based Deep Learning Model |
| title_full | Wind Speed Forecasting with a Clustering-Based Deep Learning Model |
| title_fullStr | Wind Speed Forecasting with a Clustering-Based Deep Learning Model |
| title_full_unstemmed | Wind Speed Forecasting with a Clustering-Based Deep Learning Model |
| title_short | Wind Speed Forecasting with a Clustering-Based Deep Learning Model |
| title_sort | wind speed forecasting with a clustering based deep learning model |
| topic | wind speed forecasting Dirichlet mixture model dynamic time warping clustering LSTM |
| url | https://www.mdpi.com/2076-3417/12/24/13031 |
| work_keys_str_mv | AT fuatkosanoglu windspeedforecastingwithaclusteringbaseddeeplearningmodel |