Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm
The current paper investigates several methods of predicting wind energy generation for the onshore “La Haute Borne” wind farm. The hybrid model has been developed to get short-term power forecasts using both historical in-situ measurements available from ENGIE and Modern-Era Retrospective Analysis...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508122000382 |
| _version_ | 1811188142110146560 |
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| author | Radmila Mandzhieva Rimma Subhankulova |
| author_facet | Radmila Mandzhieva Rimma Subhankulova |
| author_sort | Radmila Mandzhieva |
| collection | DOAJ |
| description | The current paper investigates several methods of predicting wind energy generation for the onshore “La Haute Borne” wind farm. The hybrid model has been developed to get short-term power forecasts using both historical in-situ measurements available from ENGIE and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) global reanalysis dataset.Meteorology is connected with chemistry because weather is the state of air, determined by pressure and temperature variations. This means that enriching historical measurements with weather data has a potential to get better predictions. It was shown that adding three extra meteorological parameters – pressure, humidity, and temperature – allowed to reach a higher accuracy compared with cases when weather parameters were completely ignored or used partially; this was proved by applying multivariate, one-step Long Short-Term Memory (LSTM) networks.Finally, the paper explains how to apply LSTM networks for the day-ahead forecasts. In particular, two state-of-the-art models were investigated – a base LSTM network and a more advanced method which combines convolutional and LSTM layers through the CNN-LSTM approach. Results showed the latter network reached higher accuracy for both 12- and 24-h forecasts and performed faster than an ordinary LSTM network. A significant advantage of both methods deals with their light structure which allows running models on the Central Processing Unit (CPU). |
| first_indexed | 2024-04-11T14:15:44Z |
| format | Article |
| id | doaj.art-4e58a26e87634ec38ddb2edf1c6d82b7 |
| institution | Directory Open Access Journal |
| issn | 2772-5081 |
| language | English |
| last_indexed | 2024-04-11T14:15:44Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj.art-4e58a26e87634ec38ddb2edf1c6d82b72022-12-22T04:19:33ZengElsevierDigital Chemical Engineering2772-50812022-09-014100048Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farmRadmila Mandzhieva0Rimma Subhankulova1Corresponding author.; World Energy Expert Group, Moscow 121359, RussiaWorld Energy Expert Group, Moscow 121359, RussiaThe current paper investigates several methods of predicting wind energy generation for the onshore “La Haute Borne” wind farm. The hybrid model has been developed to get short-term power forecasts using both historical in-situ measurements available from ENGIE and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) global reanalysis dataset.Meteorology is connected with chemistry because weather is the state of air, determined by pressure and temperature variations. This means that enriching historical measurements with weather data has a potential to get better predictions. It was shown that adding three extra meteorological parameters – pressure, humidity, and temperature – allowed to reach a higher accuracy compared with cases when weather parameters were completely ignored or used partially; this was proved by applying multivariate, one-step Long Short-Term Memory (LSTM) networks.Finally, the paper explains how to apply LSTM networks for the day-ahead forecasts. In particular, two state-of-the-art models were investigated – a base LSTM network and a more advanced method which combines convolutional and LSTM layers through the CNN-LSTM approach. Results showed the latter network reached higher accuracy for both 12- and 24-h forecasts and performed faster than an ordinary LSTM network. A significant advantage of both methods deals with their light structure which allows running models on the Central Processing Unit (CPU).http://www.sciencedirect.com/science/article/pii/S2772508122000382Onshore wind farmMeteorologyShort-term forecastsDeep learningLSTMCNN-LSTM |
| spellingShingle | Radmila Mandzhieva Rimma Subhankulova Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm Digital Chemical Engineering Onshore wind farm Meteorology Short-term forecasts Deep learning LSTM CNN-LSTM |
| title | Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm |
| title_full | Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm |
| title_fullStr | Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm |
| title_full_unstemmed | Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm |
| title_short | Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm |
| title_sort | data driven applications for wind energy analysis and prediction the case of la haute borne wind farm |
| topic | Onshore wind farm Meteorology Short-term forecasts Deep learning LSTM CNN-LSTM |
| url | http://www.sciencedirect.com/science/article/pii/S2772508122000382 |
| work_keys_str_mv | AT radmilamandzhieva datadrivenapplicationsforwindenergyanalysisandpredictionthecaseoflahautebornewindfarm AT rimmasubhankulova datadrivenapplicationsforwindenergyanalysisandpredictionthecaseoflahautebornewindfarm |