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خوشهبندی لوازم خانگی با استفاده از مدل خوشهبندی سلسله مراتبی بر اساس ویژگیهای لوازم خانگی | ||
| مدل سازی در مهندسی | ||
| دوره 22، شماره 78، آبان 1403، صفحه 283-296 اصل مقاله (1.02 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.28906.2359 | ||
| نویسندگان | ||
| شیما سیمسار1؛ محمود البرزی* 2؛ علی رجب زاده قطری3؛ علی یزدیان ورجانی4 | ||
| 1دانشجوی گروه مدیریت فناوری اطلاعات، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران | ||
| 2دانشیارگروه مدیریت فناوری اطلاعات، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران | ||
| 3دانشیار گروه مدیریت صنعتی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 4دانشیارگروه مهندسی قدرت، دانشگاه تربیت مدرس، تهران، ایران | ||
| تاریخ دریافت: 19 آبان 1401، تاریخ بازنگری: 10 آذر 1402، تاریخ پذیرش: 30 بهمن 1402 | ||
| چکیده | ||
| امروزه پاسخ تقاضا به عنوان عنصر مهم در قابلیت اطمینان شبکه هوشمند شناخته شده است. سیستمهای مدیریت انرژی خانههای هوشمند که راهاندازی وسایل برقی را با توجه به ضرورت استفاده و کارایی در اولویت قرار میدهند، نقشی حیاتی در اثربخشی استراتژیهای پاسخ تقاضا دارند. شفافسازی جزئیات مصرف برق در قبوض با وجود فناوریهای حسگر به نظارت بهینه استفاده از لوازم خانگی کمک میکند. در این تحقیق، یک مدل یادگیری ماشین بدون نظارت، برای خوشهبندی لوازم خانگی بهمنظور مدیریت قبوض برق بر اساس ویژگیهای ذاتی آنها مطرح گردید، چرا که بر این اساس میتوان جزئیات مصرف به ازای هر خوشه از لوازم خانگی را در قبض مصرفی دوره لحاظ کرد و به دلیل وجود خوشههای محدود برای لوازم خانگی امکان مدیریت و نظارت بر مصرف برق امکان پذیر میگردد. لوازم خانگی با روش خوشهبندی سلسله مراتبی به سه خوشه طبقهبندی شدند. خوشه اول لوازمی است که به صلاحدید مشتری بلافاصله روشن شوند، خوشه دوم طبق برنامه زمانبندی روشن میشوند و استفاده از آنها میتواند به تعویق افتد و خوشه سوم لوازمی هستند که توسط تعداد محدودی از مشتریان ترجیح داده میشوند. ضریب silhouette بعنوان معیاری برای ارزیابی عملکرد مدل خوشهبندی سلسله مراتبی ایجاد شد، که مقدار 0.56رضایتبخشی مدل را نشان میدهد. بر اساس نتایج، مشخص شد که روش خوشهبندی پیشنهادی میتواند با انتخاب ویژگیهای مناسب، انواع مختلف لوازم خانگی را بطور منطقی طبقهبندی کند، زیرا لوازم موجود در یک خوشه شبیه به یکدیگر هستند و میتوانند به کاربران در درک عملکرد لوازم خانگی کمک کنند. | ||
| کلیدواژهها | ||
| شبکه هوشمند؛ سیستم مدیریت انرژی خانگی؛ پاسخ تقاضا؛ خوشهبندی سلسه مراتبی | ||
| عنوان مقاله [English] | ||
| Hierarchical Clustering of Residential Appliances Considering the Characteristics of the Appliances | ||
| نویسندگان [English] | ||
| Shima Simsar1؛ Mahmood Alborzi2؛ Ali Rajabzadeh Ghatari3؛ Ali Yazdian4 | ||
| 1PhD Student, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Associate Professor, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 3Associate Professor, Department of Industrial Management, Tarbiat Modares University, Tehran, Iran | ||
| 4Associate Professor, Department of Power Engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده [English] | ||
| Nowadays, demand response is recognized as an important element in the reliability of smart grid. Smart home energy management systems, which prioritize the start-up of electrical appliances according to the necessity of use and efficiency, play a vital role in the effectiveness of load response strategies in residential areas. Considering the sensor technologies, clarification on electricity consumption details helps to optimally monitor how the appliances are used. In this research, an unsupervised machine learning model was proposed for the clustering of home appliances to manage the bills of customers based on their inherent characteristics. Due to the small number of clusters, it becomes possible to manage electricity consumption. The hierarchical clustering method was used to classify appliances into three clusters. The first cluster is the appliances that are turned on at the discretion of the consumers immediately, the second cluster is the appliances that can be turned on according to the schedule and their usage can be postponed and the third cluster is appliances that are preferred by a limited number of consumers. The silhouette coefficient was developed as a measure of the hierarchical clustering model performance, where the average silhouette coefficient of 0.56 indicates the satisfaction of the model. Based on the results, it was found that the proposed clustering method can rationally classify different types of home appliances by selecting the appropriate characteristics since the appliances in a cluster are very similar to each other and can help users understand the operating conditions of the appliances. | ||
| کلیدواژهها [English] | ||
| Smart grid, Home energy Management system, Demand response, Hierarchical clustering | ||
| مراجع | ||
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