Hybrid ConvLSTM Model for Wind Speed Prediction: A Case Study for India's Highest Wind Speed Cities
Özet
Wind speed prediction is very important for logistics, energy production and renewable energy sources, aviation and maritime, agriculture, disaster management, environmental monitoring, construction, life planning and economic activities. In this study, it was aimed to predict the wind speeds of Jaisalmer, Kochi, Mangalore, Puri and Rameswaram, which are among the cities with the highest wind speeds in India. ConvLSTM hybrid model was developed by taking advantage of the effective features of CNN and LSTM models for wind speed prediction. With ConvLSTM, it is aimed to better determine the dynamics of wind speed by processing spatial and temporal data simultaneously. ConvLSTM has been extensively tested using an approximately 15-year hourly dataset consisting of RF, SVM, ANFIS, CNN and LSTM and wind speed measurements at 10 meters and 100 meters’ altitudes. Experimental results show that ConvLSTM has R2 values above 0.9 for almost all of the city and wind speed parameters, and outperforms the compared models with the lowest error values in terms of RMSE and MAE error metrics.
Anahtar Kelimeler
- OpenAlex
- 1
- TR Dizin
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Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| dc.contributor.author | Utku, Anıl | |
| dc.contributor.author | Akyol, Sinem | |
| dc.date.accessioned | 2026-10-28T15:11:26.631947467Z | |
| dc.date.available | 2026-10-28T15:11:26.631947467Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Wind speed prediction is very important for logistics, energy production and renewable energy sources, aviation and maritime, agriculture, disaster management, environmental monitoring, construction, life planning and economic activities. In this study, it was aimed to predict the wind speeds of Jaisalmer, Kochi, Mangalore, Puri and Rameswaram, which are among the cities with the highest wind speeds in India. ConvLSTM hybrid model was developed by taking advantage of the effective features of CNN and LSTM models for wind speed prediction. With ConvLSTM, it is aimed to better determine the dynamics of wind speed by processing spatial and temporal data simultaneously. ConvLSTM has been extensively tested using an approximately 15-year hourly dataset consisting of RF, SVM, ANFIS, CNN and LSTM and wind speed measurements at 10 meters and 100 meters’ altitudes. Experimental results show that ConvLSTM has R2 values above 0.9 for almost all of the city and wind speed parameters, and outperforms the compared models with the lowest error values in terms of RMSE and MAE error metrics. | |
| dc.identifier.doi | 10.18586/msufbd.1561443 | |
| dc.identifier.eissn | 2149-6455 | |
| dc.identifier.endpage | 215 | |
| dc.identifier.issn | 2147-7930 | |
| dc.identifier.issue | 2 | |
| dc.identifier.startpage | 201 | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/munzur/6264 | |
| dc.identifier.volume | 12 | |
| dc.language.iso | tr | |
| dc.relation.ispartof | Muş Alparslan Üniversitesi Fen Bilimler Dergisi | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Mühendislik, Elektrik ve Elektronik | |
| dc.subject | Bilgisayar Bilimleri, Yapay Zeka | |
| dc.title | Hybrid ConvLSTM Model for Wind Speed Prediction: A Case Study for India's Highest Wind Speed Cities | |
| dc.type | Article | |
| rdl.citation.openalex | 1 | |
| rdl.citation.trdizin | 1 | |
| rdl.oa.status | gold | |
| rdl.oa.url | https://doi.org/10.18586/msufbd.1561443 | |
| rdl.sdg | SDG13 | |
| rdl.sdg | SDG11 | |
| rdl.sdg | SDG09 | |
| rdl.source.index | TRDIZIN |




