||Appliance Recognition Using a Density-based Clustering Approach with Multiple Granularities
||Department of Engineering Science
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.
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
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