JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1039 10.6339/22-JDS1039 Data Science in Action A Hybrid Monitoring Procedure for Detecting Abnormality with Application to Energy Consumption Data LimDaeyoung1 https://orcid.org/0000-0003-1935-2447 ChenMing-Huiming-hui.chen@uconn.edu1∗ RavishankerNalini1 BolducMark2 McKeonBrian2 NolanStanley2 Department of Statistics, University of Connecticut, 215 Glenbrook Rd. U-4120, Storrs, CT 06269-4120, United States Facilities Operations, University of Connecticut, 25 LeDoyt Road U-3252, Storrs, CT 06269-3252, United States Corresponding author. Email: ming-hui.chen@uconn.edu. 20221632022202135155 Supplementary Material

An R package for our method can be found at https://github.com/daeyounglim/energystuff. This repository contains R functions running our proposed method, an R program for generating simulation data sets, and another R wrapper function simplifying user interface for when running simulations over a large number of data sets.

111220211322022 2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2022 Open access article under the CC BY license.

The complexity of energy infrastructure at large institutions increasingly calls for data-driven monitoring of energy usage. This article presents a hybrid monitoring algorithm for detecting consumption surges using statistical hypothesis testing, leveraging the posterior distribution and its information about uncertainty to introduce randomness in the parameter estimates, while retaining the frequentist testing framework. This hybrid approach is designed to be asymptotically equivalent to the Neyman-Pearson test. We show via extensive simulation studies that the hybrid approach enjoys control over type-1 error rate even with finite sample sizes whereas the naive plug-in method tends to exceed the specified level, resulting in overpowered tests. The proposed method is applied to the natural gas usage data at the University of Connecticut.

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