Non-intrusive Appliance Monitoring and Concurrent Activity Recognition from Circuit-Level Power Consumption
Date Issued
2010
Date
2010
Author(s)
Lin, Gu-Yuan
Abstract
With the innovation of appliances and the emphasis on the quality of life, the variety of appliances is increasing, which also results the increment of energy consumption. Due to the convenience and automation of appliances, residents are usually unaware of the waste of electricity. On the other hand, since most home activities are related to the use of appliances, this exhibits the possibility of using appliance operating states to further infer home activities.
I proposes a 2-layer framework which use the a non-intrusive way to install power meters to measure circuit-level power consumption and then use such consumption to monitor the appliance states and take one step ahead to recognize concurrent home activities. We can utilize such information to analyze the use of electricity and further provide more effective and practical power-saving services. However, there are two challenges for the implementation of the framework. First, when there are different appliance combinations consuming similar total power loads, distinguishing them only based on the power loads is easy to make erroneous prediction. Second, we want to verify the feasibility of just taking appliance states to infer concurrent activities.
To overcome the first challenge mentioned above, I advocate using the Factorial Conditional Random Fields(FCRFs) to accommodate the co-temporal relationships among multiple appliances and to discriminate those different appliance combinations with similar total power consumption. For the second challenge, I also employ FCRFs and additionally consider the pre-local relationships among the previous appliance state-change events to improve the capability of identifying concurrent activities.
In both a laboratory and a practical dormitory environment, I deploy power meters to measure the circuit-level power consumption and collect the annotations of activities for the comparisons of recognition accuracy between FCRFs, PCRFs, and other commonly used classi⣸00;ers, including Naive Bayes, AdaBoost, and Support Vector Machine(SVM).
The experimental results of appliance recognition show that FCRFs has the highest joint accuracy in both environments, which demonstrates that the co-temporal relationships are helpful. On the other hand, in activity recognition, despite FCRFs perform badly than SVM in a real environment, the results still show that considering pre-local features dramatically improves the recognition accuracy. Furthermore, the best joint accuracy obtained from SVM is 98.35%, which demonstrates that the system has great feasibility in practical home environments.
Subjects
Factorial Conditional Random Fields
Parallel Conditional Random Fields
Non-intrusive
Appliance Recognition
Activity Recognition
Type
thesis
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