Angela HUO received the B.Eng and Ph.D. degrees from Northeastern University, China in 2002 and 2007, both in Computer Science and Technology. Her research interests include data stream management technology, advanced data analysis, and data-driven cybersecurity.
From 2012 to 2014, Angela HUO taught at the Department of Computer Information System, the University of the Fraser Valley in Canada, and studied at the University of Waterloo as a visiting scholar for one year.
Angela HUO has been awarded the National Outstanding Instructor five times for organizing the students and coaching the teams to win the Champions and Runner-ups in National Software Innovation Contests, ACM-ICPC Asia Regional Contests, World Odyssey of the Mind China Finals, Shanghai International Blockchain Hackathon, Technovation for girls programming since 2011.
Can supervise: YES
Liu, X, Iftikhar, N, Huo, H, Li, R & Nielsen, PS 2019, 'Two approaches for synthesizing scalable residential energy consumption data', Future Generation Computer Systems, vol. 95, pp. 586-600.View/Download from: UTS OPUS or Publisher's site
© 2019 Elsevier B.V. Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method. They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation. This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets.