Short-term prediction of wind power density using convolutional LSTM network
Efficient extraction of renewable energy from wind depends on the reliable estimation of wind characteristics and optimization of wind farm installation and operation conditions. There exists uncertainty in the prediction of wind energy tapping potential based on the variability in wind behavior. Th...
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
University of Belgrade - Faculty of Mechanical Engineering, Belgrade
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/84fcd3961924415e96e1f4239c7721c1 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:84fcd3961924415e96e1f4239c7721c1 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:84fcd3961924415e96e1f4239c7721c12021-12-05T21:01:45ZShort-term prediction of wind power density using convolutional LSTM network1451-20922406-128X10.5937/fme2103653Ghttps://doaj.org/article/84fcd3961924415e96e1f4239c7721c12021-01-01T00:00:00Zhttps://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2021/1451-20922103653G.pdfhttps://doaj.org/toc/1451-2092https://doaj.org/toc/2406-128XEfficient extraction of renewable energy from wind depends on the reliable estimation of wind characteristics and optimization of wind farm installation and operation conditions. There exists uncertainty in the prediction of wind energy tapping potential based on the variability in wind behavior. Thus the estimation of wind power density based on empirical models demand subsequent data processing to ensure accuracy and reliability in energy computations. Present study analyses the reliability of the ANN-based machine learning approach in predicting wind power density for five stations (Chennai, Coimbatore, Madurai, Salem, and Tirunelveli) in the state of Tamil Nadu, India using five different non-linear models. The selected models such as Convolutional Neural Network (CNN), Dense Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short Term Memory (LSTM) Network, and linear regression are employed for comparing the data for a period from Jan 1980 to May 2018. Based on the results, it was found that the performance of (1->Conv1D|2->LSTM|1-dense) is better than the other models in estimating wind power density with minimum error values (based on mean absolute error and root mean squared error).Gupta DeepakKumar VikasAyus IshanVasudevan M.Natarajan N.University of Belgrade - Faculty of Mechanical Engineering, Belgradearticlewind powerbidirectional lstmcnn lstmlinear regressionEngineering (General). Civil engineering (General)TA1-2040Mechanics of engineering. Applied mechanicsTA349-359ENFME Transactions, Vol 49, Iss 3, Pp 653-663 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
wind power bidirectional lstm cnn lstm linear regression Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359 |
| spellingShingle |
wind power bidirectional lstm cnn lstm linear regression Engineering (General). Civil engineering (General) TA1-2040 Mechanics of engineering. Applied mechanics TA349-359 Gupta Deepak Kumar Vikas Ayus Ishan Vasudevan M. Natarajan N. Short-term prediction of wind power density using convolutional LSTM network |
| description |
Efficient extraction of renewable energy from wind depends on the reliable estimation of wind characteristics and optimization of wind farm installation and operation conditions. There exists uncertainty in the prediction of wind energy tapping potential based on the variability in wind behavior. Thus the estimation of wind power density based on empirical models demand subsequent data processing to ensure accuracy and reliability in energy computations. Present study analyses the reliability of the ANN-based machine learning approach in predicting wind power density for five stations (Chennai, Coimbatore, Madurai, Salem, and Tirunelveli) in the state of Tamil Nadu, India using five different non-linear models. The selected models such as Convolutional Neural Network (CNN), Dense Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short Term Memory (LSTM) Network, and linear regression are employed for comparing the data for a period from Jan 1980 to May 2018. Based on the results, it was found that the performance of (1->Conv1D|2->LSTM|1-dense) is better than the other models in estimating wind power density with minimum error values (based on mean absolute error and root mean squared error). |
| format |
article |
| author |
Gupta Deepak Kumar Vikas Ayus Ishan Vasudevan M. Natarajan N. |
| author_facet |
Gupta Deepak Kumar Vikas Ayus Ishan Vasudevan M. Natarajan N. |
| author_sort |
Gupta Deepak |
| title |
Short-term prediction of wind power density using convolutional LSTM network |
| title_short |
Short-term prediction of wind power density using convolutional LSTM network |
| title_full |
Short-term prediction of wind power density using convolutional LSTM network |
| title_fullStr |
Short-term prediction of wind power density using convolutional LSTM network |
| title_full_unstemmed |
Short-term prediction of wind power density using convolutional LSTM network |
| title_sort |
short-term prediction of wind power density using convolutional lstm network |
| publisher |
University of Belgrade - Faculty of Mechanical Engineering, Belgrade |
| publishDate |
2021 |
| url |
https://doaj.org/article/84fcd3961924415e96e1f4239c7721c1 |
| work_keys_str_mv |
AT guptadeepak shorttermpredictionofwindpowerdensityusingconvolutionallstmnetwork AT kumarvikas shorttermpredictionofwindpowerdensityusingconvolutionallstmnetwork AT ayusishan shorttermpredictionofwindpowerdensityusingconvolutionallstmnetwork AT vasudevanm shorttermpredictionofwindpowerdensityusingconvolutionallstmnetwork AT natarajann shorttermpredictionofwindpowerdensityusingconvolutionallstmnetwork |
| _version_ |
1718371012409556992 |