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Article Citation
@article{https://doi.org/10.1002/sta4.513, author = {Chen, Ming-Hui and Lim, Daeyoung and Ravishanker, Nalini and Linder, Henry and Bolduc, Mark and McKeon, Brian and Nolan, Stanley}, title = {Collaborative analysis for energy usage monitoring and management on a large university campus}, journal = {Stat}, volume = {11}, number = {1}, pages = {e513}, keywords = {aggregated monthly data, client and consultant interaction, consulting services, data visualization and web application, high-frequency data}, doi = {https://doi.org/10.1002/sta4.513}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sta4.513}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/sta4.513}, abstract = {This describes a successful ongoing collaboration between the Department of Statistics and the Facilities Operations at the University of Connecticut (UConn). The collaboration stemmed from facilities reaching out to the productive Statistical Consulting Services (SCS) in the Department of Statistics for help in understanding and optimizing energy usage across the large university campus. Starting on a small scale with one faculty leader and two graduate assistants in 2016, the continued high level of deliverables resulted in the project growing in personnel and scope to involve four statistics faculty collaborators, four engineers and operations staff from the facilities unit, one staff from Office of Budget and Planning, one postdoc, ten PhD graduate assistants, four MS graduate students, five UConn undergraduate students, and two international visiting undergraduate researchers. This paper highlights this journey and explains how the concerted effort and team work continues to pave the way for useful implementation, many-tiered mentoring, interesting research projects, and team satisfaction of a job well done.}, year = {2022} }
Article Citation
@article{lim_chen_ravishanker_bolduc_mckeon_nolan_2022, author = {Daeyoung Lim and Ming-Hui Chen and Nalini Ravishanker and Mark Bolduc and Brian McKeon and Stanley Nolan}, title = {A Hybrid Monitoring Procedure for Detecting Abnormality with Application to Energy Consumption Data}, journal = {Journal of Data Science}, volume = {20}, number = {2}, year = {2022}, pages = {135--155}, doi = {10.6339/22-JDS1039}, issn = {1680-743X}, publisher = {School of Statistics, Renmin University of China}, }
Featured Publications
Bayesian analysis of spherically parameterized dynamic multivariate stochastic volatility models (2022, Computational Statistics)
Guanyu Hu, Ming-Hui Chen, Nalini Ravishanker
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DOI
Collaborative Analysis for Energy Usage Monitoring and Management on a Large University Campus (2022, Stat)
Ming-Hui Chen, Daeyoung Lim, Nalini Ravishanker, M. Henry Linder, Mark Bolduc, Brian McKeon, Stanley Nolan
A collaboration between Facilities Operations and the Department of Statistics at UConn on energy monitoring.
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DOI
A Hybrid Monitoring Procedure for Detecting Abnormality with Application to Energy Consumption Data (2022, Journal of Data Science)
Daeyoung Lim, Ming-Hui Chen, Nalini Ravishanker, Mark Bolduc, Brian McKeon, Stanley Nolan
A hybrid (frequentist and Bayesian) and computationally intensive approach to anomaly detection, applied to natural gas usage data from UConn.
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Anomaly Detection in Energy Usage Patterns (2021, arXiv)
Henry Linder, Nalini Ravishanker, Ming-Hui Chen, David McIntosh, Stanley Nolan
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DOI