پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 646 |
| تعداد مقالات | 9,451 |
| تعداد مشاهده مقاله | 68,217,340 |
| تعداد دریافت فایل اصل مقاله | 47,087,268 |
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 | ||
|
[1] S. Subhasis, S. Mohanty, P.K. Rout, B.K. Sahu, M.Bajaj, H.M. Zawbaa, and S. Kamel. "Residential Demand Side Management model, optimization and future perspective: A review." Energy Reports 8, (2022): 3727-3766. [2] P. Siano. "Demand response and smart grids –a survey." Renewable and Sustainable Energy Reviews 30, (2014): 461-478. [3] M. Muratori, B.A. Schuelke-Leech, and G. Rizzoni. "Role of residential demand response in modern electricity markets." Renew Sustain Energy Rev 33, (2014): 546-553. [4] C. Delmastro, E. Lavagno, and G. Mutani. "Chinese residential energy demand: scenarios to 2030 and policies implication." Energy Build 89, (2015): 49-60. [5] H.T. Haider, O.H. See, and W. Elmenreich. "A review of residential demand response of smart grid." Renewable and Sustainable Energy Reviews 59, (2016): 166-178. [6] S. Ahmadi, and S.M.T. Bathaee. "Modeling and simulation of Energy managements for fuel cell hybrid vehicle." Journal of Modeling in Engineering 15, no. 50 (2017): 1-16. [7] X. Xue, S. Wang, Y. Sun, and F. Xiao. "An interactive power demand management strategy for facilitating smart grid optimization." Appl Energy 116, (2014): 297–310. [8] P. Warren. " A review of demand-side management policy in the UK." Renew Sustain EnergyRev 29, (2014): 941-951. [9] M.S. Ahmed, A. Mohamed, T. Khatib, H. Shareef, R.Z. Homod, and J.A. Ali. "Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm." Energy and Buildings 138, (2017): 27-215. [10] M. Muratori, and G. Rizzoni. "Residential Demand Response: Dynamic Energy Management and Time-Varying Electricity Pricing." IEEE Transactions on Power Systems 31, no. 2 (2016): 1108-1117. [11] B. Yu, F. Sun, C. Chen, G. Fu, and L. Hu. "Power demand response in the context of smart home application." Energy 240, (2022): 122774. [12] S. Wilkinson, K. Hojckova, C. Eon, G.M. Morrison and B. Sandén. "Is peer-to-peer electricity trading empowering users? Evidence on motivations and roles in a prosumer business model trial in Australia." Energy Research & Social Science 66, (2020): 101500. [13] M. Alizade, M. Jaafari, and Y. Soltan Moradi. "Smart home optimized energy management considering energy storage, solar cell, electric vehicle and load response." Journal of Modeling in Engineering 17, no. 57 (2019): 215-226. (in Persian) [14] Y. Leroy and B. Yannou. "An activity-based modeling framework for quantifying occupants’ energy consumption in residential buildings." Computers in Industry 103, (2018): 1-13. [15] P. Wang, Zh. Zhang, L. Fu, and N. Ran. "Optimal design of home energy management strategy based on refined load model." Energy 218, (2021): 119516. [16] H.T. Haider, D.H. Muhsen, Y.M. Al-Nidawi, T. Khatib, and O.H. See. "A novel approach for multi-objective cost-peak optimization for demand response of a residential area in smart grids." Energy 254, Part B, (2022): 124360. [17] Y. Deng, F. Luo, Y. Zhang and Y. Mu. "An efficient energy management framework for residential communities based on demand pattern clustering." Applied Energy 347 (2023): 121408. [18] M.Z. Oskouei, B.M. Ivatloo, M. Abapour, A.A. Moghaddam, and H.Mehrjerdi. "Practical implementation of residential load management system by considering vehicle-for-power transfer: Profit analysis." Sustainable Cities and Society 60 (2020): 3102144. [19] Y. Liu, H. Li, J. Zhu, Y. Lin and W. Lei. "Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm." Energy 262, Part A (2023): 125460. [20] E. Shirazi and S.H. Jadid. "Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS." Energy and Buildings 93 (2015): 40-49. [21] S. Javaid, N. Javaid, M.S. Javaid, S. Javaid, U. Qasim, and Z. Ali Khan. " Optimal scheduling in smart homes with energy storage using appliances super-clustering." 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Fukuoka, Japan, p. 342-348. 2016. [22] Z. Iqbal, N. Javaid, S. Iqbal, S.H. Aslam and Z. Ali Khan. " A Domestic Microgrid with Optimized Home Energy Management System." Energies 11, no. 4 (2018). [23] M. Li, G.Y. Li, S. Iqbal, H.R. Chen and C.W. Jiang. "QoE-Aware Smart Home Energy Management Considering." Energies 11, no. 9 (2018). [24] N.U. Hassan, Y.I. Khalid, C. Yuen, S. Huang, and M.A. Pasha. " Framework for minimum user participation rate determination to achieve specific demand response management objectives in the residential smart grids." International Journal of Electrical Power & Energy Systems 74, (2016): 91–103. [25] M. Danish, N. Javaid, N. Alrajeh, Z. Ali Khan, U. Qasim, I. Ahmed, and M. Ilahi. "Realistic Scheduling Mechanism for Smart Homes." Energies 9, no. 3 (2016): 202. [26] M.B. Rasheed, N. Javaid, A. Ahmad, M. Jamil and Z. Ali Khan. "Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing." Energies 9, no. 8 (2016). [27] Z. Zhu, Y. Lin, W. Lei, Y. Liu and M. Tao. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm." Energy 171, (2019): 944-955. [28] F. McLoughlin, A. Duffy, and M. Conlon. "A clustering approach to domestic electricity load profile characterisation using smart metering data." Energy 141, (2015): 190-199. [29] M. Saunders, P. Lewis, and A. Thornhill. Research Methods for Business Students. 5th ed., Financial Times Prentice Hall press, 2009. [30] M. Alborzi and M. Alikhani. Machine Learning. Sharif University of Technology Scientific Publication, 2018. (in Persian) [31] Z. Wang and R.S. Srinivasan. "Classification of Household Appliance Operation Cycles: A Case-Study Approach." Energies 8, no. 9 (2015): 10522-10536. [32] S.H. Abudalfa and M. Mikki. "K-means algorithm with a novel distance measure." Turkish Journal of Electrical Engineering and Computer Sciences 21, no. 6 (2013): 1665-1684. [33] G. Chhabra, V. Vashisht, and J. Ranjan. "Crime Prediction Patterns Using Hybrid K-Means Hierarchical Clustering." Journal of Advanced Research in Dynamical and Control Systems 11, (2019): 1249-1258. [34] C. Semeraro, M. Lezoche, H. Panetto and M. Dassisti. "Digital twin paradigm: A systematic literature review." Computers in Industry 130, (2021). [35] D. Chris and He. Xiaofeng. "Cluster Merging and Splitting in Hierarchical Clustering Algorithms." ICDM '02: Proceedings of the 2002 IEEE International Conference on Data Mining, 2002. [36] J. Tamas. "Hierarchical Clustering based on IndoorGML Document." IEEE 2019 15th International Scientific Conference on Informatics, Poprad, Slovakia, p. 177-182, 2019. [37] Z. Wang and Ravi S. Srinivasan. "Classification of Household Appliance Operation Cycles: A Case-Study Approach." Energies 8, no. 9 (2015): 10522-10536. | ||
|
آمار تعداد مشاهده مقاله: 259 تعداد دریافت فایل اصل مقاله: 262 |
||