ارائه یک رویکرد جدید پایش غیر مداخله‌گر بار بر اساس استخراج ماتریس ویژگی و مدل یادگیری ماشین KNN

الموضوعات :

بهروز طاهری ¹ (گروه مهندسی برق، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران)
مصطفی صدیقی زاده ² (دانشکده مهندسی برق، دانشگاه شهید بهشتی، تهران، ایران)
محمدرضا نصیری ³ (گروه مهندسی برق، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران)
علیرضا شیخی فینی ⁴ (گروه پژوهشی برنامه‌ریزی و بهره‌برداری سیستم قدرت، پژوهشگاه نیرو، تهران، ایران)

تاريخ الإرسال : 18 السبت , محرم, 1445 تاريخ التأكيد : 05 الجمعة , ربيع الثاني, 1445 تاريخ الإصدار : 10 الثلاثاء , شعبان, 1445

الکلمات المفتاحية: استخراج ویژگی, پایش غیر مداخله‌گر بار, KNN, تبدیل هیلبرت, ماتریس ویژگی, فرکانس لحظه‌ای,

ملخص المقالة :

در سال‌های اخیر علاقه به انجام تحقیقات بر روی پایش غیر مداخله‌گر بار به دلیل افزایش مصرف انرژی الکتریکی به ‌شدت در حال افزایش است. تحقیقات مختلف نشان داده‌اند که در صورت پیاده‌سازی روش‌های پایش غیر مداخله‌گر بار به‌غیراز مزایای مختلفی چون پاسخگویی بار، افزایش دقت پیش‌بینی‌های مصرف و غیره موجب افزایش سطح صرفه‌جویی در ساکنان ساختمان‌های مسکونی خواهد شد. در سال‌های اخیر با پیشرفت روش‌های مبتنی بر یادگیری عمیق استفاده از این روش‌ها نیز به‌منظور تفکیک بار مصرفی بسیار افزایش پیداکرده است. با این‌ وجود مهم‌ترین مشکل این روش‌ها نیاز به سخت‌افزار پیچیده به‌منظور آموزش و بررسی روش‌ها است. به همین دلیل نیاز است تا سیگنال توان نمونه‌برداری شده از کنتور هوشمند به مراکز پردازش داده منتقل‌شده و مورد تجزیه‌وتحلیل قرار گیرد. این کار علاوه بر نیاز به شبکه‌های ارتباطی پرسرعت امنیت داده‌ها را نیز به خطر می‌اندازد. با توجه به نکات بیان‌شده در این مقاله یک روش پایش غیر مداخله‌گر بار بر اساس استخراج ماتریس ویژگی از سیگنال فرکانس لحظه‌ای به‌دست‌آمده از سیگنال توان لوازم‌خانگی ارائه ‌شده است. مهم‌ترین ویژگی روش ارائه ‌شده افزایش دقت مدل نزدیک‌ترین همسایه (KNN) کلاسیک است. روش ارائه ‌شده با استفاده از داده‌های دسترسی آزاد با نام EMBED که شامل اطلاعات مصرف سه آپارتمان مختلف است مورد تجزیه‌وتحلیل قرارگرفته است. نتایج به‌خوبی نشان می‌دهد که مدل KNN در زمان استفاده از داده‌ها ماتریس ویژگی مورداستفاده در این مقاله از دقت بسیار بالاتری در مقابل دیگر روش‌های استخراج ویژگی برخوردار است.

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