الصفحة الرئيسية

كلية العلوم \ الرياضيات

منال الزين هلايلي

نسبة اكتمال الملف الشخصي
الجنسية التونسية
التخصص العام الرياضيات
التخصص الدقيق تحليل توافقي
المسمى الوظيفي أستاذ مساعد
الدرجة العلمية (المرتبة) دكتوراه

نبذه مختصرة

استاذ مساعد بقسم الرياضيات كلية العلوم جامعة تبوك

المؤهلات العلمية

بكالوريوس رياضيات ماجستير تخصص تحليل توافقي دكتوراه تخصص تحليل توافقي

الاهتمامات البحثية

Uncertainty principles of integral Transforms Study of q-Special functions like Macdonald and the third Bessel Functions Appllications of q-Fourier Transform

الخبرات والمناصب الإدارية

لا يوجد

الجدول الدراسي
اليوم المادة الوقت
من إلى
الإثنين برمجة رياضية 11:00 12:30
الإثنين تحليل حقيقي 14:00 15:30
الأربعاء برمجة رياضية 11:00 12:30
الأربعاء تحليل حقيقي 14:00 15:30
الأبحاث والمؤلفات
  • Time-frequency analysis of localization operators for the non-isotropic n-dimensional modified Stockwell transform
  • CALDERON-REPRODUCING FORMULA FOR THE CONTINUOUS WAVELET TRANSFORM RELATED TO THE WEINSTEIN OPERATOR
  • Continuous Stockwell Transform and Uncertainty principle Related to Riemann Liouville Operator
  • Toeplitz Operators for Stockwell Transform related to the Spherical mean Operator
  • Generalized $q$-Bessel Operator
  • A Varation On Uncertainty Principles For The Generalized $q$-Bessel Fourier Transform
  • On Nash and Carlsons inequalities for symmetric $q$- integral transforms
  • Toeplitz operators for Stockwell transform related to the spherical mean operator
  • Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model
  • Existence of solution for fractional differential equations involving symmetric fuzzy numbers
  • Some Uncertainty Principles for the Right-Sided Multivariate Continuous Quaternion Wavelet Transform
جوائز التميز
  • لا يوجد
المشاريع البحثية
اسم المشروع وصف المشروع
Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model first_pageDownload PDFsettingsOrder Article Reprints Open AccessArticle Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model by Elham M Al-Ali 1,*,Yassine Hajji 2,Yahia Said 3,4ORCID,Manel Hleili 1,Amal M Alanzi 1,Ali H Laatar 5 andMohamed Atri 6ORCID 1 Mathematics Department, College of Sciences, Tabuk University, Tabuk 71491, Saudi Arabia 2 Laboratory of Energetics and Thermal and Mass Transfer (LR01ES07), Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 1068, Tunisia 3 Remote Sensing Unit, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia 4 Laboratory of Electronics and Microelectronics (LR99ES30), University of Monastir, Monastir 5019, Tunisia 5 Physics Department, College of Sciences, Tabuk University, Tabuk 71491, Saudi Arabia 6 College of Computer Sciences, King Khalid University, Abha 62529, Saudi Arabia * Author to whom correspondence should be addressed Mathematics 2023, 11(3), 676 https://doiorg/103390/math11030676 Submission received: 11 January 2023 / Revised: 23 January 2023 / Accepted: 27 January 2023 / Published: 28 January 2023 (This article belongs to the Special Issue Artificial Intelligence in Geoenvironmental and Energy Sciences) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Green energy is very important for developing new cities with high energy consumption, in addition to helping environment preservation Integrating solar energy into a grid is very challenging and requires precise forecasting of energy production Recent advances in Artificial Intelligence have been very promising Particularly, Deep Learning technologies have achieved great results in short-term time-series forecasting Thus, it is very suitable to use these techniques for solar energy production forecasting In this work, a combination of a Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) network, and a Transformer was used for solar energy production forecasting Besides, a clustering technique was applied for the correlation analysis of the input data Relevant features in the historical data were selected using a self-organizing map The hybrid CNN-LSTM-Transformer model was used for forecasting The Fingrid open dataset was used for training and evaluating the proposed model The experimental results demonstrated the efficiency of the proposed model in solar energy production forecasting Compared to existing models and other combinations, such as LSTM-CNN, the proposed CNN-LSTM-Transformer model achieved the highest accuracy The achieved results show that the proposed model can be used as a trusted forecasting technique that facilitates the integration of solar energy into grids
معلومات التواصل
البريد الإلكتروني : mhleili@UT.EDU.SA
0595018245