Over the past year, a team of several SCS members has developed statistical techniques and data visualizations of energy use on the UConn campus. The project, a collaboration with the University’s Utilities Services & Energy Management within Facilities Operations & Building Services, predicts the energy consumption of campus buildings in order to identify maintenance problems at the UConn campus. SCS has applied a variety of statistical tools—statistical control charts, mixed effects time series models, cluster analysis—to a subset of the data, at a monthly frequency. The resulting data visualizations and modeling pipeline are implemented in an interactive web application, and SCS has begun to apply these models and exploratory software to a second dataset: a high-frequency dataset of all energy use on campus.
The consulting team continues to meet regularly with representatives of the Utility Operations & Energy Management group at UConn, including Stanley Nolan (Director), David McIntosh (Associate Directory) , Mark Bolduc, and Brian McKeon.
The project was supervised by Professor Ming-Hui Chen (firstname.lastname@example.org). Active consultants in Fall 2017 were PhD students Renjie Chen (email@example.com) and Henry Linder (firstname.lastname@example.org); and undergraduate student Francisco Cifuentes Villarroel ’17 (email@example.com). Previously, PhD student Aditya Mishra ’17 worked on the project, as well as MS students Shaochen Bai, Yile Huang, Yueqi Liu, and Haining Zhang.
|Professor Ming-Hui Chen||Renjie Chen||Henry Linder|