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

فناوری های نوین مهندسی برق در سیستم انرژی سبز

دوره 2، شماره 4 - شماره پیاپی 8، اسفند 1402، صفحه 108-127   

نوع مقاله: مقاله پژوهشی

شناسه دیجیتال (DOI): 10.30486/teeges.2023.1995779.1093

نویسندگان

بهروز طاهری¹؛ مصطفی صدیقی زاده^{* 2}؛ محمدرضا نصیری¹؛ علیرضا شیخی فینی³

¹گروه مهندسی برق، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران

²دانشکده مهندسی برق، دانشگاه شهید بهشتی، تهران، ایران

³گروه پژوهشی برنامه‌ریزی و بهره‌برداری سیستم قدرت، پژوهشگاه نیرو، تهران، ایران

چکیده

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.

کلیدواژه‌ها

پایش غیر مداخله‌گر بار؛ استخراج ویژگی؛ ماتریس ویژگی؛ فرکانس لحظه‌ای؛ تبدیل هیلبرت؛ KNN

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