Transforming Wind Nowcasting: Innovative Strategies for Next-Frame Prediction Using Conv-LSTM-3D Model.
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| Title: | Transforming Wind Nowcasting: Innovative Strategies for Next-Frame Prediction Using Conv-LSTM-3D Model. |
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| Authors: | B. Upadhyay, Abhay1 abhayupadhyay.rs@silveroakuni.ac.in, R. Shah, Saurin1, A. Thakkar, Rajesh1 |
| Source: | Iraqi Journal for Electrical & Electronic Engineering. Jun2026, Vol. 22 Issue 1, p36-45. 10p. |
| Subjects: | Wind forecasting, Deep learning, Machine learning, Forecasting, Spatiotemporal processes, Weather forecasting |
| Abstract (English): | This research paper presents an innovative approach to wind nowcasting, addressing specific performance parameters through advanced machine learning techniques. The research aims to overcome inherent challenges in capturing intricate spatiotemporal relationships within wind data. Our novel methodology integrates Conv-LSTM-3D models, emphasizing the prediction of next-frame wind patterns. The Conv-LSTM-3D architecture, combining 3D convolutions and LSTM networks, is specifically tailored to effectively learn temporal dependencies and spatial features in wind data. The introduction outlines the pressing issues associated with traditional wind nowcasting methods, emphasizing the need for improved accuracy and prediction reliability. The primary objectives of this study are to explore the potential of Conv-LSTM-3D models in enhancing wind nowcasting and to assess their performance against traditional methods. Through comprehensive experiments, our approach demonstrates significant improvements in critical performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Specifically, improvements of 0.01, 19.23, 0.11, and 0.64 are observed, highlighting the enhanced accuracy and prediction reliability in the context of next-frame wind nowcasting. Notably, the system achieves these advancements within a reduced time frame, taking only 1149 seconds. This research contributes significantly to the advancement of meteorological prediction techniques, offering a refined short-term wind forecasting tool with potential applications across various fields. The improved clarity and organization of our methodology and findings pave the way for more effective utilization of Conv-LSTM-3D models in enhancing wind nowcasting capabilities. [ABSTRACT FROM AUTHOR] |
| Abstract (Arabic): | يركز المقال على تطوير التنبؤ الفوري بالرياح من خلال ابتكار وتقييم نموذج جديد يُدعى الشبكة التلافيفية طويلة المدى ثلاثية الأبعاد (Conv-LSTM-3D)، المصمم لتوقع نمط الرياح في الإطار الزمني التالي. من خلال دمج عمليات الالتفاف ثلاثية الأبعاد مع شبكات الذاكرة طويلة المدى (LSTM)، يتمكن النموذج من التقاط التبعيات المكانية والزمانية المعقدة في بيانات الرياح بشكل أكثر فعالية مقارنةً بأساليب التعلم الآلي التقليدية مثل الانحدار الخطي، وآلات الدعم الناقل، وخوارزمية الجار الأقرب (k-nearest neighbors). تُظهر النتائج التجريبية أن نموذج Conv-LSTM-3D يُحسن بشكل ملحوظ مؤشرات الأداء الرئيسية، بما في ذلك متوسط الخطأ التربيعي (Mean Squared Error - MSE)، وجذر متوسط الخطأ التربيعي (Root Mean Squared Error - RMSE)، ونسبة الإشارة إلى الضوضاء القصوى (Peak Signal-to-Noise Ratio - PSNR)، ومؤشر التشابه الهيكلي (Structural Similarity Index - SSIM)، مع الحفاظ على أوقات معالجة فعالة. تستخدم الدراسة مجموعات بيانات أرصاد جوية مُنسقة بصيغة NetCDF، ومقسمة زمنياً لأغراض التدريب والاختبار، وتُبرز إمكانيات النموذج في تحسين دقة التنبؤات القصيرة الأمد للرياح، مع تطبيقات في مجال الأرصاد الجوية والمجالات ذات الصلة. [Extracted from the article] |
| Copyright of Iraqi Journal for Electrical & Electronic Engineering is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Transforming Wind Nowcasting: Innovative Strategies for Next-Frame Prediction Using Conv-LSTM-3D Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22B%2E+Upadhyay%2C+Abhay%22">B. Upadhyay, Abhay</searchLink><relatesTo>1</relatesTo><i> abhayupadhyay.rs@silveroakuni.ac.in</i><br /><searchLink fieldCode="AR" term="%22R%2E+Shah%2C+Saurin%22">R. Shah, Saurin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22A%2E+Thakkar%2C+Rajesh%22">A. Thakkar, Rajesh</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Iraqi+Journal+for+Electrical+%26+Electronic+Engineering%22">Iraqi Journal for Electrical & Electronic Engineering</searchLink>. Jun2026, Vol. 22 Issue 1, p36-45. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: This research paper presents an innovative approach to wind nowcasting, addressing specific performance parameters through advanced machine learning techniques. The research aims to overcome inherent challenges in capturing intricate spatiotemporal relationships within wind data. Our novel methodology integrates Conv-LSTM-3D models, emphasizing the prediction of next-frame wind patterns. The Conv-LSTM-3D architecture, combining 3D convolutions and LSTM networks, is specifically tailored to effectively learn temporal dependencies and spatial features in wind data. The introduction outlines the pressing issues associated with traditional wind nowcasting methods, emphasizing the need for improved accuracy and prediction reliability. The primary objectives of this study are to explore the potential of Conv-LSTM-3D models in enhancing wind nowcasting and to assess their performance against traditional methods. Through comprehensive experiments, our approach demonstrates significant improvements in critical performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Specifically, improvements of 0.01, 19.23, 0.11, and 0.64 are observed, highlighting the enhanced accuracy and prediction reliability in the context of next-frame wind nowcasting. Notably, the system achieves these advancements within a reduced time frame, taking only 1149 seconds. This research contributes significantly to the advancement of meteorological prediction techniques, offering a refined short-term wind forecasting tool with potential applications across various fields. The improved clarity and organization of our methodology and findings pave the way for more effective utilization of Conv-LSTM-3D models in enhancing wind nowcasting capabilities. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Arabic) Group: Ab Data: يركز المقال على تطوير التنبؤ الفوري بالرياح من خلال ابتكار وتقييم نموذج جديد يُدعى الشبكة التلافيفية طويلة المدى ثلاثية الأبعاد (Conv-LSTM-3D)، المصمم لتوقع نمط الرياح في الإطار الزمني التالي. من خلال دمج عمليات الالتفاف ثلاثية الأبعاد مع شبكات الذاكرة طويلة المدى (LSTM)، يتمكن النموذج من التقاط التبعيات المكانية والزمانية المعقدة في بيانات الرياح بشكل أكثر فعالية مقارنةً بأساليب التعلم الآلي التقليدية مثل الانحدار الخطي، وآلات الدعم الناقل، وخوارزمية الجار الأقرب (k-nearest neighbors). تُظهر النتائج التجريبية أن نموذج Conv-LSTM-3D يُحسن بشكل ملحوظ مؤشرات الأداء الرئيسية، بما في ذلك متوسط الخطأ التربيعي (Mean Squared Error - MSE)، وجذر متوسط الخطأ التربيعي (Root Mean Squared Error - RMSE)، ونسبة الإشارة إلى الضوضاء القصوى (Peak Signal-to-Noise Ratio - PSNR)، ومؤشر التشابه الهيكلي (Structural Similarity Index - SSIM)، مع الحفاظ على أوقات معالجة فعالة. تستخدم الدراسة مجموعات بيانات أرصاد جوية مُنسقة بصيغة NetCDF، ومقسمة زمنياً لأغراض التدريب والاختبار، وتُبرز إمكانيات النموذج في تحسين دقة التنبؤات القصيرة الأمد للرياح، مع تطبيقات في مجال الأرصاد الجوية والمجالات ذات الصلة. [Extracted from the article] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Iraqi Journal for Electrical & Electronic Engineering is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.37917/ijeee.22.1.4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 36 Subjects: – SubjectFull: Wind forecasting Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Spatiotemporal processes Type: general – SubjectFull: Weather forecasting Type: general Titles: – TitleFull: Transforming Wind Nowcasting: Innovative Strategies for Next-Frame Prediction Using Conv-LSTM-3D Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: B. Upadhyay, Abhay – PersonEntity: Name: NameFull: R. Shah, Saurin – PersonEntity: Name: NameFull: A. Thakkar, Rajesh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18145892 Numbering: – Type: volume Value: 22 – Type: issue Value: 1 Titles: – TitleFull: Iraqi Journal for Electrical & Electronic Engineering Type: main |
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