Imputation based wind speed forecasting technique during abrupt changes in short term scenario
Abstract It is tough and complex to forecast wind speed due to its intermittent and stochastic nature as well as sudden and abrupt variations in the wind speed. Further, it is required to handle the variety of scenarios e.g. cyber‐attacks, unexpected power device malfunction, communication/sensor ou...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.13124 |
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| author | Karan Sareen Bijaya Ketan Panigrahi Tushar Shikhola Ravi Nath Tripathi Ashok Kumar Rajput |
| author_facet | Karan Sareen Bijaya Ketan Panigrahi Tushar Shikhola Ravi Nath Tripathi Ashok Kumar Rajput |
| author_sort | Karan Sareen |
| collection | DOAJ |
| description | Abstract It is tough and complex to forecast wind speed due to its intermittent and stochastic nature as well as sudden and abrupt variations in the wind speed. Further, it is required to handle the variety of scenarios e.g. cyber‐attacks, unexpected power device malfunction, communication/sensor outages etc. that can cause the missing data.This paper proposes and employs a de‐noising autoencoder algorithm for wind speed forecasting to ensure the handling of missing data information. At the next step, the data is processed via variational mode decomposition technique to mitigate the noise and improves the model's prediction accuracy. Furthermore, the bi‐directional long‐short term memory deep learning approach is tied with convolution neural network to increase prediction accuracy and anticipating the sudden/abrupt changes in wind speed accurately. Finally, actual wind speed related data is examined to scrutinize meticulousness of projected forecast methodology particularly during sudden/abrupt changes in the wind speed. The parameter indicators of the wind speed forecasting technique exhibit the capability of improved predictions under the diversified conditions. |
| format | Article |
| id | doaj-art-31f4bc31e6554d249112a22e10231d1f |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-31f4bc31e6554d249112a22e10231d1f2025-01-10T17:41:03ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142751277210.1049/rpg2.13124Imputation based wind speed forecasting technique during abrupt changes in short term scenarioKaran Sareen0Bijaya Ketan Panigrahi1Tushar Shikhola2Ravi Nath Tripathi3Ashok Kumar Rajput4Central Electricity Authority, Ministry of PowerGovt. of IndiaDelhi IndiaDepartment of Electrical EngineeringIndian Institute of Technology DelhiDelhi IndiaDepartment of Electrical EngineeringIndian Institute of Technology DelhiDelhi IndiaNext Generation Power Electronics Research Center Kyushu Institute of TechnologyFukuoka JapanCentral Electricity Authority, Ministry of PowerGovt. of IndiaDelhi IndiaAbstract It is tough and complex to forecast wind speed due to its intermittent and stochastic nature as well as sudden and abrupt variations in the wind speed. Further, it is required to handle the variety of scenarios e.g. cyber‐attacks, unexpected power device malfunction, communication/sensor outages etc. that can cause the missing data.This paper proposes and employs a de‐noising autoencoder algorithm for wind speed forecasting to ensure the handling of missing data information. At the next step, the data is processed via variational mode decomposition technique to mitigate the noise and improves the model's prediction accuracy. Furthermore, the bi‐directional long‐short term memory deep learning approach is tied with convolution neural network to increase prediction accuracy and anticipating the sudden/abrupt changes in wind speed accurately. Finally, actual wind speed related data is examined to scrutinize meticulousness of projected forecast methodology particularly during sudden/abrupt changes in the wind speed. The parameter indicators of the wind speed forecasting technique exhibit the capability of improved predictions under the diversified conditions.https://doi.org/10.1049/rpg2.13124technological forecastingwind power |
| spellingShingle | Karan Sareen Bijaya Ketan Panigrahi Tushar Shikhola Ravi Nath Tripathi Ashok Kumar Rajput Imputation based wind speed forecasting technique during abrupt changes in short term scenario IET Renewable Power Generation technological forecasting wind power |
| title | Imputation based wind speed forecasting technique during abrupt changes in short term scenario |
| title_full | Imputation based wind speed forecasting technique during abrupt changes in short term scenario |
| title_fullStr | Imputation based wind speed forecasting technique during abrupt changes in short term scenario |
| title_full_unstemmed | Imputation based wind speed forecasting technique during abrupt changes in short term scenario |
| title_short | Imputation based wind speed forecasting technique during abrupt changes in short term scenario |
| title_sort | imputation based wind speed forecasting technique during abrupt changes in short term scenario |
| topic | technological forecasting wind power |
| url | https://doi.org/10.1049/rpg2.13124 |
| work_keys_str_mv | AT karansareen imputationbasedwindspeedforecastingtechniqueduringabruptchangesinshorttermscenario AT bijayaketanpanigrahi imputationbasedwindspeedforecastingtechniqueduringabruptchangesinshorttermscenario AT tusharshikhola imputationbasedwindspeedforecastingtechniqueduringabruptchangesinshorttermscenario AT ravinathtripathi imputationbasedwindspeedforecastingtechniqueduringabruptchangesinshorttermscenario AT ashokkumarrajput imputationbasedwindspeedforecastingtechniqueduringabruptchangesinshorttermscenario |