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Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

Published: 04 November 2015 Publication History

Abstract

Anomaly detection is an important problem in building energy management in order to identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies from the genuine changes in energy consumption due to seasonal variations and changes in personal settings such as holidays. One of the important drawbacks of existing anomaly detection algorithms is that various unknown context variables, such as seasonal variations, can affect the energy consumption of users in ways that appear anomalous to existing time series based anomaly detection algorithms.
In this paper, we present a system for monitoring the energy consumption of multiple users within a neighborhood and a novel algorithm for detecting anomalies by combining data from multiple users. For each user, the neighborhood is defined as the set of all other users that have similar characteristics (function, location or demography), and are therefore likely to react and consume energy in the similar way in response to the external conditions. The neighborhood can be predefined based on prior customer information, or can be identified through an analysis of historical energy consumption. The proposed algorithm works as a two-step process. In the first step, the algorithm periodically computes an anomaly score for each user by just considering their own energy consumption and variations in the consumption of the past. In the second step, the anomaly score for a user is adjusted by analyzing the energy consumption data in the neighborhood. The collation of data within the neighborhood allows the proposed algorithm to differentiate between these genuine effects and real anomalous behavior of users. Unlike multivariate time series anomaly detection algorithms, the proposed algorithm can identify specific users that are exhibiting anomalous behavior. The capabilities of the algorithm are demonstrated using several year-long real-world data sets, for commercial as well as residential consumers.

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  1. Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

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    cover image ACM Conferences
    BuildSys '15: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments
    November 2015
    264 pages
    ISBN:9781450339810
    DOI:10.1145/2821650
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 04 November 2015

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    Author Tags

    1. anomaly detection
    2. context information
    3. energy monitoring

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    Funding Sources

    • Department Of Electronics & Information Technology, Government Of India

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    BuildSys '15 Paper Acceptance Rate 20 of 66 submissions, 30%;
    Overall Acceptance Rate 148 of 500 submissions, 30%

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    • (2024)Low Runtime Approach for Fault Detection for Refrigeration Systems in Smart Homes Using Wavelet TransformIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332814770:1(4447-4456)Online publication date: Feb-2024
    • (2024)Neural Architecture Search for Anomaly Detection in Time-Series Data of Smart Buildings: A Reinforcement Learning Approach for Optimal Autoencoder DesignIEEE Internet of Things Journal10.1109/JIOT.2024.336088211:10(18059-18073)Online publication date: 15-May-2024
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    • (2023)A survey of anomaly detection methods for power gridsInternational Journal of Information Security10.1007/s10207-023-00720-z22:6(1799-1832)Online publication date: 8-Jul-2023
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