Skip to main content

Performance Evaluation of Techniques for Identifying Abnormal Energy Consumption in Buildings

Resource type
Date created
2019-05-08
Authors/Contributors
Abstract
Energy consumption in buildings has steadily increased. Buildings consume more energy than necessary due to suboptimal design and operation. Apart from retro-fitting, not much can be done with the design of the existing building, but the operation of the building can be improved. Ignoring or failing to fix the faults can lead to problems like the higher cost in excess energy usage or premature component failure. At the same time understanding, identifying, and addressing abnormal energy consumption in buildings can lead to energy savings and detection of faulty appliances. This paper investigates two key challenges found in energy anomaly detection research: 1) the lack of labeled ground truth and 2) the lack of consistent performance accuracy metrics. In the first challenge, labeled ground truth is imperative for training and benchmarking algorithms to detect anomalies. In the second challenge, consistent performance accuracy metrics are crucial to quantifying how well algorithms perform against each other. There exists no publicly available energy consumption dataset with labeled anomaly events. Therefore, we propose two approaches that help in the automatic annotation of the ground truth data from publicly available datasets: a statistical approach for short-term data and a piecewise linear regression method for long-term data. We demonstrate these approaches using two publicly available datasets called Dataport (Pecan Street) and HUE. Using different existing accuracy metrics, we run a series of experiments on anomaly detection algorithms and discuss what metrics can be best used for consistent accuracy testing amongst researchers. In addition, while providing the source code, we also release an anomaly annotated dataset produced by this source code.
Document
Published as
Gaur, Megha & Makonin, Stephen & V. Bajic, Ivan & Majumdar, Angshul. (2019). Performance evaluation of techniques for identifying abnormal energy consumption in buildings. IEEE Access. PP. 1-1. DOI: 10.1109/ACCESS.2019.2915641
Publication title
IEEE Access
Document title
Performance evaluation of techniques for identifying abnormal energy consumption in buildings
Date
2019
First page
1
Last page
1
Publisher DOI
10.1109/ACCESS.2019.2915641
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
English
Member of collection
Download file Size
08709671.pdf 8.97 MB

Views & downloads - as of June 2023

Views: 0
Downloads: 0