Jackson Codispoti, a computer science major in the University Scholars Program, will publish an article as first author in the Association for Computing Machinery (ACM) academic journal Transactions on Cyber-Physical Systems (TCPS). "Learning from Non-Experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes" will appear in an upcoming issue.
TCPS is the premier journal for the publication of high-quality original research papers and survey papers that have scientific and technological understanding of the interactions of information processing, networking and physical processes.
Codispoti conducts research in associate professor Simone Silvestri’s laboratory. He is supported by a Research Experiences for Undergraduates (REU) grant from the National Science Foundation. After finishing his undergraduate studies, Codispoti plans to pursue a Ph.D. here at UK.
The abstract for Codispoti’s article is below:
With the acceleration of ICT technologies and the Internet of Things (IoT) paradigm, smart residential environments, also known as smart homes, are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition. Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances' electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert, always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this paper, we develop a SAL algorithm, called K-Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness, representativeness and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency.