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系統識別號 U0026-2208201920192600
論文名稱(中文) 以多精細度之密度分群演算法實現電器辨識
論文名稱(英文) Appliance Recognition Using a Density-based Clustering Approach with Multiple Granularities
校院名稱 成功大學
系所名稱(中) 工程科學系
系所名稱(英) Department of Engineering Science
學年度 107
學期 2
出版年 108
研究生(中文) 顏均惟
研究生(英文) Chun-Wei Yen
學號 n96061101
學位類別 碩士
語文別 英文
論文頁數 35頁
口試委員 指導教授-鄧維光
口試委員-侯廷偉
口試委員-王明習
口試委員-曹譽鐘
中文關鍵字 電器辨識  資料分群  智慧插座 
英文關鍵字 appliance recognition  data clustering  smart socket 
學科別分類
中文摘要 有鑑於現有的電力儲能效率仍未臻完美,且在用電高峰時段仍可能發生供電短缺的問題,大多數的電力公司鼓勵其客戶選用時間電價方案,以分散尖峰時段的用電需求至離峰時段,但此一構想之落實須讓各用戶能即時地掌握其用電資訊;此外,隨著物聯網技術的進步,用戶端的智慧插座成為當今管理家庭用電的一種常見方式,然而現有的智慧插座僅能呈現插座上所有電器之總和而非單一電器之用電量。在本研究中,我們深入地探究了電器辨識之議題,並將非監督式演算法整合到模組化的智慧插座中,藉此可識別出插座上的不同電器;具體而言,我們提出以多精細度之密度分群演算法進行電器辨識,藉由調節不同精細度來試著區分電力特徵近似的不同電器,而根據實驗探討之結果,我們的方法在沒有新電器的先備知識情況下亦能達到有效的辨識成果,本研究亦開發了具使用者操作介面之原型系統,使用者可以觀看單一電器的即時功耗,透過與使用者的互動,我們的系統能藉此學習並訓練得到更好的辨識效果。
英文摘要 Electricity may not be economically stored as other forms of energy such that it would be in short supply during the peak time. In view of this difficulty, most power suppliers encourage their customers to adopt time-of-use rate plans. Consequently, it is essential for a user to be able to perceive the real-time information of power consumption. With the advancement of Internet of Things technologies, smart sockets are becoming a commodity to manage power consumption in a household. However, current smart sockets merely present the total electricity consumption rather than the individual consumption of household appliances. In this work, we thus investigate the problem of appliance recognition and implement an unsupervised algorithm on a modular smart socket so as to identify each appliance on the socket. Specifically, we propose to adopt a density-based clustering approach to perform this appliance recognition task. Furthermore, appliances with similar load signatures (or power features) can be identified by considering different data granularities in our approach. Experimental studies show that our approach is feasible even when there is no prior knowledge of new appliances. We also develop a prototype system with graphical user interfaces to present the real-time power consumption of individual appliance. With the user interaction, our system can learn from the user feedback.
論文目次 Chapter 1 Introduction 1
1.1 Motivation and Overview 1
1.2 Contributions of This Work 2
Chapter 2 Preliminaries 3
2.1 Smart Sockets on the Market 3
2.2 Load Signatures 5
2.3 Problem Definition of Appliance Recognition 5
2.4 Review of Appliance Recognition Techniques 6
2.4.1 Classification Approaches 7
2.4.2 Event-based Clustering Approaches 8
2.4.3 Density-based Clustering Approaches 9
Chapter 3 Design of the Smart Socket and the Recursive Density-based Clustering Approach 11
3.1 Design of Our Smart Socket 11
3.2 Obtaining Electrical Load Signatures 13
3.3 Proposed Density-based Clustering Approach 14
3.4 Process of Our Appliance Recognition Scheme 16
3.4.1 Load Change Detection 17
3.4.2 Load Disaggregation 18
3.4.3 Labeling of Unknown Appliances 19
3.4.4 Representation of a Cluster 19
3.4.5 Recursion with Different Granularities 19
Chapter 4 Prototyping and Empirical Studies 22
4.1 Collecting Data Samples 22
4.2 Hardware Implementation of Our Smart Socket 24
4.3 Experiment #1: Using Different Parameter Settings in DBSCAN 26
4.4 Experiment #2: Recursive DBSCAN with Multiple Granularities 27
4.5 User Interactions 30
Chapter 5 Conclusions and Future Works 31
Bibliography 32
參考文獻 [1] K. Ma, C. Wang, J. Yang, Z. Tian, and X. Guan, X, “Energy Management Based on Demand-side Pricing: A Super Modular Game Approach,” IEEE Access, 5:18219-18228, August 2017.
[2] M. Li, W. Gu, W. Chen, Y. He, Y. Wu, and Y. Zhang, “Smart Home: Architecture, Technologies and Systems,” Procedia Computer Science, Vol. 131:393-400, 2018.
[3] P. Palensky, and D. Dietrich, “Demand Side Management: Demand Response Intelligent Energy Systems and Smart Loads,” IEEE Transactions on Industrial Informatics, 7(3):381-388, August 2011.
[4] Q. Yan, Y. Jiancheng, K. Xiangyu, W. Xudong, M. Yuying, and L. Bo, ”Home Smart Energy Network Configuration Considerations for Demand Response,” IEEE In 2018 China International Conference on Electricity Distribution, pp. 2875-2879, September 2018.
[5] D. -M. Han, and J. -H. Lim, “Smart Home Energy Management System Using IEEE 802.15.4 and Zigbee Communication,” IEEE Transactions on Consumer Electronics, 56.3:1403-1410, October 2010.
[6] Ó Blanco-Novoa, T. Fernández-Caramés, P. Fraga-Lamas, and L. Castedo, “An Electricity Price-aware Open-source Smart Socket for the Internet of Energy,” Sensors, 17.3:643, March 2017.
[7] A. Ridi, C. Gisler, and J. Hennebert, “A Survey on Intrusive Load Monitoring for Appliance Recognition,” IEEE Proceedings of the 22nd International Conference on Pattern Recognition, pp.3702-3707, December 2014.
[8] A. Faruqui, S. Sergici, and A. Sharif, “The Impact of Informational Feedback on Energy Consumption—A Survey of the Experimental Evidence,” Energy, vol. 35, no. 4, pp. 1598-1608, April 2010.
[9] J. –S. Lee, Y. –W. Su, and C. –C. Shen, "A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi," Industrial Electronics Society, 5:46-51, November 2007.
[10] J. Chen, K. Hu, Q. Wang, Y. Sun, Z. Shi, and S. He, "Narrowband Internet of Things: Implementations and Applications," IEEE Internet of Things Journal 4.6:2309-2314, October 2017.
[11] A. Zoha, A. Gluhak, M. Imran, and S. Rajasegarar, “Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey,” Sensors, 12.12:16838-16866, December 2012.
[12] I. Abubakar, S. -N. Khalid, M. -W. Mustafa, H. Shareef, and M. Mustapha, “Application of Load Monitoring in Appliances’ Energy Management–A Review,” Renewable and Sustainable Energy Reviews, 67:235-245, January 2017.
[13] H. -H. Chang, C. -L. Lin, and H. -T. Yang, “Load Recognition for Different Loads with the Same Real Power and Reactive Power in a Non-intrusive Load-monitoring System,” International Conference on Computer Supported Cooperative Work in Design, pp.1122-1127, June 2008.
[14] S. Kong, Y. Kim, R. Ko, and S. -K. Joo, “Home Appliance Load Disaggregation Using Cepstrum-smoothing-based Method,” IEEE Transactions on Consumer Electronics, 61.1:24-30, March 2015.
[15] Y. –L. Ke, “A Lightweight Power Monitoring Module for Appliance Recognition,” January, 2019.
[16] G. Hoogsteen, J. -O. Krist, V. Bakker, and G. -J. Smit. "Non-intrusive Appliance Recognition," 3rd IEEE PES Innovative Smart Grid Technologies Europe, October 2012.
[17] N. Henao, K. Agbossou, S. Kelouwani, Y. Dubé, M. Fournier, "Approach in Nonintrusive Type I Load Monitoring Using Subtractive Clustering," IEEE Transactions on Smart Grid, 8.2:812-821, August 2015.
[18] H. -Y. Lam, G. -S. K. Fung, and W. -K. Lee, “A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signatures,” IEEE Transactions on Consumer Electronics, 53.2:653-660, July 2007.
[19] P. Meehan, S. Phelan, C. McArdle, and S. Daniels, “Temporal and Frequency Analysis of Power Signatures for Common Household Appliances,” IET Proceedings of the Symposium on ICT and Energy Efficiency and Workshop on Information Theory and Security, pp.22-27, May 2012.
[20] V. Abeykoon, N. Kankanamdurage, A. Senevirathna, P. Ranaweera. and R. Udawalpola, “Real Time Identification of Electrical Devices through Power Consumption Pattern Detection,” Proceedings of the International Conference on Micro and Nano Technologies, Modelling and Simulation, pp.1-3, 2016.
[21] M. Ester, H. -P. Kriegel, J. Sander, and X. Xu, “A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp.226-231, 1996.
[22] E Schubert, J Sander, M Ester, H. -P. Kriegel and X. Xu, “DBSCAN Revisited, Revisited: Why and How You Should (still) Use DBSCAN,” ACM Transactions on Database Systems, 42.3:19, August 2017.
[23] H. -P. Kriegel, P. Kröger, J. Sander, and A. Zimek, “Density‐based Clustering,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1.3:231-240, April 2011.
[24] M. Ankerst, M. M. Breunig, H. P. Kriegel, and J. Sander, “OPTICS: Ordering Points to Identify the Clustering Structure,” ACM Sigmod Record, Vol. 28, No. 2, pp.49-60, June 1999.
[25] U. Hunkeler, H. –L. Truong, and A. Stanford-Clark, “MQTT-S—A Publish/subscribe Protocol for Wireless Sensor Networks,” IEEE 3rd International Conference on Communication Systems Software and Middleware and Workshops, pp. 791-798, January 2008.
[26] B. Zhao, L. Stankovic, and V. Stankovic, "On a Training-less Solution for Non-intrusive Appliance Load Monitoring using Graph Signal Processing," IEEE Access 4:1784-1799, April 2016.
[27] K Koiliaris, and C Xu "A faster pseudopolynomial time algorithm for subset sum," Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, pp.1062-1072, 2017.
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