Optimized LSTM Neural Networks Model applied for Solar PV Power Prediction
DOI:
https://doi.org/10.51646/jsesd.v15iMME.340الكلمات المفتاحية:
Photovoltaic power, ، prediction, ، LSTM neural networks, ، Weighted Linear Regression.الملخص
أدت التحديات المرتبطة بالطاقة والمناخ إلى زيادة توليد الطاقة الشمسية نظرا إليجابياتها المتعددة. ومع ذلك، تبقى هذه الطاقة متقطعة وغير ثابتة مما يعقد إدماجها في شبكة التزود بالطاقة. وقصد حل هذه المشكلة، تعتبر التقنيات التوقعية الخاصة بألواح الطاقة الشمسية ذا ت أهمية قصوى قصد ضمان أمان أنظمة هذه المصادر الطاقية وخفض التكاليف المرتبطة بمحاولة رفع فعاليتها. وفي هذا اإلطار، تم إدماج تقنيات الذكاء الصناعي لتوقع ا لطاقة المولدة عن طريق األلواح الشمسية. وهذه التقنيات المقترحة هي نموذج االنحدار الخطي المرجح ونموذج الذ اكرة القصيرة األمد الطويلة والشبكات العصبية. وتسمح هذه المناهج من الحصول على توقعات عالية الدقة والموثوقية والصرامة بموازاة مع توفير شفافية كاملة. وأما بخصوص البيانات المعتمدة في الدراسة الحالية فقد تم استقاؤها من ألواح طاقة شمسية تم وضعها في موقع جغرافي محدد. وأما بخصوص المتغيرات المعتمدة، فقد تم األخذ بعين االعتبار كال من اإلشعاع الشمسي، وح اررة الجو بالموقع، ودرجات الح اررة، وسرعة الرياح واتجاهها، ومستوى الرطوبة. وقد تم التحقق من دقة وقدرة نموذج الذاكرة القصيرة األمد الطويلة ألجل تفسير التباينات في المعطيا ت وذلك بمقارنة توقعاته بنموذج تحليل االنحدار الخطي المرجح، وكل هذا تم اعتمادا على: جذور متوسط األخطاء المعيارية المربعة، ومتوسط الخطأ المطلق ومعامل االنحدار. وبينت النتائج أن تحليل االنحدار الخطي المرجح يقدم توقعات مقبولة للطاقة الشمسية المولدة بينما يشتغل نموذج الذاكرة القصيرة األمد الطويلة بشكل فعال من حيث قدرته على تحسين دقة وموثوقية التوقعات.
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