A New Approach of the Machine Learning Framework Integrating Policy Design to Predict Renewable Electricity Penetration in Resource-Constrained Settings

المؤلفون

  • Hanan Ramadhan Saad Ramadhan Electrical Engineering Department, Higher Institute of Science and Technology, Ajdabiya , Libya المؤلف
  • Magdah Othman Mohammed Osman Systems analysis and programming Department, Higher Institute of Science and Technology, Ajdabiya , Libya المؤلف
  • Llahm Omar Faraj Ben Dalla Department of Electrical and Electronics Engineering, Ankara Yildirim Beyazit University, Türkiye المؤلف
  • Tarik A. Rashid Artificial Intelligence and Innovation Centre University of Kurdistan Hewler, Erbil, Iraq المؤلف
  • Magda Juma Shuayb Albaraesi Business administration Department , Kambut Higher Institute for Administrative and Financial Sciences, Tobruk, Libya المؤلف
  • Mohamed Ali Mohamed EL-sseid Department of Software Engineering, Ankara Bilim University, Türkiye المؤلف
  • Tareq Alnnale Department of Business administration, Higher Institute of Science and Technology, Raqdalin المؤلف

DOI:

https://doi.org/10.65405/gkbdpm48

الكلمات المفتاحية:

انتشار الكهرباء المتجددة؛ شبكات LSTM؛ دمج السياسات؛ مؤشرات RISE؛ الدول النامية؛ التنبؤ بالطاقة المستدامة.

الملخص

يواجه التحول نحو أنظمة الطاقة المستدامة في الاقتصادات النامية قيودًا متعددة الأوجه، تشمل محدودية الموارد المالية، ونقص القدرات المؤسسية، وتحديات تنفيذ السياسات. وتركز أساليب التنبؤ التقليدية لانتشار الكهرباء المتجددة بشكل أساسي على المتغيرات التقنية والاقتصادية، متجاهلةً الدور المحوري لأطر السياسات كمتنبئات ديناميكية. يقدم هذا البحث بنية جديدة للتعلم الآلي تدمج بشكل صريح مؤشرات السياسات الكمية المستمدة من مؤشرات البنك الدولي التنظيمية للطاقة المستدامة (RISE)، وتقييمات الجاهزية للطاقة المتجددة (RRA) الصادرة عن الوكالة الدولية للطاقة المتجددة (IRENA)، وملفات تعريف الطاقة القطرية الصادرة عن وكالة الطاقة الدولية، في شبكة ذاكرة طويلة المدى (LSTM) للتنبؤ بتوليد الكهرباء المتجددة غير الكهرومائية في 71 دولة نامية. وعلى عكس نماذج الانحدار الثابتة، يتعامل إطار البحث هذا مع متغيرات السياسات كخصائص متغيرة مع الزمن تُعدّل الديناميكيات الزمنية لمسارات تبني الطاقة المتجددة. وتتضمن بنية النموذج آليات انتباه لترجيح أبعاد السياسات وفقًا لأهميتها السياقية عبر بيئات وطنية متنوعة. أظهرت دراسة التحقق الأولية أن التنبؤ باستخدام شبكات LSTM المدمجة مع السياسات يقلل من خطأ التنبؤ بنسبة 23.7% مقارنةً بالخطوط الأساسية التقنية والاقتصادية البحتة، لا سيما في الدول التي تشهد تطورًا سريعًا في السياسات. يُرسّخ هذا العمل دور أدوات السياسات كعامل تنبؤ أساسي في التنبؤ بالطاقة المتجددة، ويُقدّم منهجية قابلة للتطبيق لتصميم سياسات طاقة قائمة على الأدلة في البيئات ذات الموارد المحدودة.

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

السيرة الشخصية للمؤلف

  • Magdah Othman Mohammed Osman، Systems analysis and programming Department, Higher Institute of Science and Technology, Ajdabiya , Libya

     

     

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التنزيلات

منشور

2026-01-12

كيفية الاقتباس

A New Approach of the Machine Learning Framework Integrating Policy Design to Predict Renewable Electricity Penetration in Resource-Constrained Settings. (2026). مجلة العلوم الشاملة, 10(ملحق 38), 2929-2950. https://doi.org/10.65405/gkbdpm48

الأعمال الأكثر قراءة لنفس المؤلف/المؤلفين