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Journal of Data Science


A Hybrid Monitoring Procedure for Detecting Abnormality with Application to Energy Consumption Data
Volume 20, Issue 2 (2022), pp. 135–155
Pub. online: 16 March 2022      Type: Data Science In Action      Open accessOpen Access
Received
11 December 2021
Accepted
13 February 2022
Published
16 March 2022

Abstract

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.

Supplementary material

 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.

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

Keywords
Bayesian computationally-intensive method frequentist hypothesis testing

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