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

محورهای موضوعی : مهندسی برق مخابرات

بهروز طاهری ¹ , مصطفی صدیقی زاده ² , محمدرضا نصیری ³ , علیرضا شیخی فینی ⁴

1 - گروه مهندسی برق، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
2 - دانشکده مهندسی برق، دانشگاه شهید بهشتی، تهران، ایران
3 - گروه مهندسی برق، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
4 - گروه پژوهشی برنامه‌ریزی و بهره‌برداری سیستم قدرت، پژوهشگاه نیرو، تهران، ایران

تاریخ دریافت : 1402/05/14 تاریخ پذیرش : 1402/07/28 تاریخ انتشار : 1402/12/01

کلید واژه: استخراج ویژگی, پایش غیر مداخله‌گر بار, KNN, تبدیل هیلبرت, ماتریس ویژگی, فرکانس لحظه‌ای,

چکیده مقاله :

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.

چکیده انگلیسی:

In recent years, the interest in conducting research on non-intrusive load monitoring is increasing strongly due to the increase in electrical energy consumption. Numerous studies have underscored that the implementation of non-intrusive load monitoring methods, apart from various advantages such as load response, increasing the accuracy of load prediction, etc., will increase the level of cost savings for occupants of residential structures. Recently, with the adoption of techniques grounded in deep learning, the use of these methods has also increased in order to load disaggregation. However, the most important problem with these methods is the need for complex hardware in order to train and examine the techniques. For this reason, it is necessary to transfer the power signal sampled from the smart meter to data processing centers and be analyzed. In addition to the need for high-speed communication networks, this also endangers data security. Accordingly, in this article, a non-intrusive load monitoring method is presented based on extracting the feature matrix from the instantaneous frequency signal obtained from the power signal of household appliances. The most important feature of the presented method is to increase the accuracy of the classical KNN model. The presented method has been analyzed using EMBED open-access data, which includes the consumption dataset from three different apartments. The results show that the KNN model attains significantly enhanced accuracy when using the feature matrix data introduced in this article compared to other feature extraction methods.

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