Anomaly Detection in Energy Usage Patterns

TL;DR

This paper presents a collaborative approach using statistical methods to detect anomalies in energy usage patterns on a university campus, aiming to improve service, reduce costs, and support green energy initiatives.

Contribution

It introduces a practical comparison of model-free and model-based statistical methods for anomaly detection in energy data within a real-world university setting.

Findings

Effective detection of anomalous energy usage patterns.

Enhanced interpretability of statistical models.

Successful implementation in a university environment.

Abstract

Energy usage monitoring on higher education campuses is an important step for providing satisfactory service, lowering costs and supporting the move to green energy. We present a collaboration between the Department of Statistics and Facilities Operations at an R1 research university to develop statistically based approaches for monitoring monthly energy usage and proportional yearly usage for several hundred utility accounts on campus. We compare the interpretability and power of model-free and model-based methods for detection of anomalous energy usage patterns in statistically similar groups of accounts. Ongoing conversation between the academic and operations teams enhances the practical utility of the project and enables implementation for the university. Our work highlights an application of thoughtful and continuing collaborative analysis using easy-to-understand statistical principles for real-world deployment.