استخدام الشبكات العصبونية في التنبؤ قصير الأمد بالحمل الكهربائي في محافظة طرطوس
Abstract
تعد التغذية الكهربائية الموثوقة والمستمرة ضرورية في ظل وظائف المجتمع الحالي المعقدة. نتيجةً للاستهلاك المتزايد وتوسع شبكات التوزيع الكهربائية، فإن نظم القدرة الكهربائية تعمل بشكل قريب من حدودها الفنية، وبالتالي تتزايد احتمالية حدوث حالات التحميل الزائد، فشل التجهيزات والتعتيم. أكثر من ذلك، فإننا نواجه مشكلة أخرى تتجسد في عدم القدرة على تخزين الطاقة الكهربائية بشكل فعال وبالتالي يجب أن يتم توليد الطاقة الكهربائية عند الحاجة لها فقط.
ونظراً لما يواجهه العالم من نضوب الموارد النفطية والصعوبات المرتبطة بتأمين مصادر أخرى لتوليد الطاقة الكهربائية فإن عملية التنبؤ بالحمل الكهربائي تشكل عاملاً حاسماً في منظومة القدرة الكهربائية سواء من الناحية الاقتصادية، أو من الناحية الفنية على مستويي التشغيل والتخطيط.
يقدم هذا البحث منظومة تنبؤ بالحمل الكهربائي قصير الأمد بالاعتماد على الشبكات العصبونية، مع محاكاة ضمن بيئة ماتلاب بالإضافة إلى واجهة بيانية للمنظومة اعتماداً على بيانات الأحمال السابقة ومحددات الطقس في محافظة طرطوس.
A reliable and continuous supply of electrical energy is necessary for the functioning of today’s complex society. Because of the increasing consumption and the extension of existing electrical transmission networks and these power systems are operated closer and closer to their limits accordingly the possibilities of overloading, equipment failures and blackout are also increasing, furthermore, we have an additional obstacle which is that electrical energy cannot be stored efficiently, so, electrical energy should be generated only when it's needed.
Due to the fact that world is facing a lack of oil reserves and the difficulties related to have alternative sources to generate electrical power, then, electrical load forecasting is considered as a crucial factor in electrical power system either from economical or technical point of view on both planning and operating levels.
This research introduces a short term electrical load forecasting system by using artificial neural networks with a simulation in Matlab environment in addition to an interface for the system and all that is depending on previous load data and weather parameters in Tartous province.
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