Wind missing data arrangement using wavelet based techniques for getting maximum likelihood
Long time series of wind data can have data gaps that may lead to errors in the subsequent analyses of the time series. This study proposes using the wavelet transform as a system to verify that a data completion technique is correct and that the data series behaves correctly, enabling the user to...
| Main Authors: | , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/15070 |
| _version_ | 1789406426086506496 |
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| author | Zapata Sierra, Antonio Jesús Cama Pinto, Alejandro Gil Montoya, Francisco Alcayde García, Alfredo Manzano Agugliaro, Francisco Rogelio |
| author_facet | Zapata Sierra, Antonio Jesús Cama Pinto, Alejandro Gil Montoya, Francisco Alcayde García, Alfredo Manzano Agugliaro, Francisco Rogelio |
| author_sort | Zapata Sierra, Antonio Jesús |
| collection | DSpace |
| description | Long time series of wind data can have data gaps that may lead to errors in the subsequent analyses
of the time series. This study proposes using the wavelet transform as a system to verify that a data
completion technique is correct and that the data series behaves correctly, enabling the user to infer the
expected results. Wind speed data from three weather stations located in southern Europe were used to
test the proposed method. The series consist of data measured every 10 minutes for 11 years. Various
techniques are used to complete the data of one of the series; the wavelet transform is used as the control
method, and its scalogram is used to visualize it. If the representation in the scalogram has zero magnitude,
it shows the absence of data, so that if the data are properly filled in, then they have similar magnitudes
to the rest of the series. The proposed method has shown that in case of data series inconsistencies, the
wavelet transform can identify the lack of accuracy of the natural periodicity of these data. This result can
be visually checked using the WT’s scalogram. Additionally, the scallograms provide valuable information
on the variables studied, e.g. periods of higher wind speed. In summary, the wavelet transform has proven
to be an excellent analysis tool that reveals the seasonal pattern of wind speed in periodograms at various
scales. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-15070 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2024 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-150702024-01-10T12:32:08Z Wind missing data arrangement using wavelet based techniques for getting maximum likelihood Zapata Sierra, Antonio Jesús Cama Pinto, Alejandro Gil Montoya, Francisco Alcayde García, Alfredo Manzano Agugliaro, Francisco Rogelio Winddata WaveletTransform FFT missingdata renewableenergy datafilling Long time series of wind data can have data gaps that may lead to errors in the subsequent analyses of the time series. This study proposes using the wavelet transform as a system to verify that a data completion technique is correct and that the data series behaves correctly, enabling the user to infer the expected results. Wind speed data from three weather stations located in southern Europe were used to test the proposed method. The series consist of data measured every 10 minutes for 11 years. Various techniques are used to complete the data of one of the series; the wavelet transform is used as the control method, and its scalogram is used to visualize it. If the representation in the scalogram has zero magnitude, it shows the absence of data, so that if the data are properly filled in, then they have similar magnitudes to the rest of the series. The proposed method has shown that in case of data series inconsistencies, the wavelet transform can identify the lack of accuracy of the natural periodicity of these data. This result can be visually checked using the WT’s scalogram. Additionally, the scallograms provide valuable information on the variables studied, e.g. periods of higher wind speed. In summary, the wavelet transform has proven to be an excellent analysis tool that reveals the seasonal pattern of wind speed in periodograms at various scales. 2024-01-10T12:32:07Z 2024-01-10T12:32:07Z 2019-01-01 info:eu-repo/semantics/article http://hdl.handle.net/10835/15070 en Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| spellingShingle | Winddata WaveletTransform FFT missingdata renewableenergy datafilling Zapata Sierra, Antonio Jesús Cama Pinto, Alejandro Gil Montoya, Francisco Alcayde García, Alfredo Manzano Agugliaro, Francisco Rogelio Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title | Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_full | Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_fullStr | Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_full_unstemmed | Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_short | Wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| title_sort | wind missing data arrangement using wavelet based techniques for getting maximum likelihood |
| topic | Winddata WaveletTransform FFT missingdata renewableenergy datafilling |
| url | http://hdl.handle.net/10835/15070 |
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