Online Anomaly Energy Consumption Detection Using Lambda Architecture

Liu, Xiufeng; Iftikhar, Nadeem; Nielsen, Per Sieverts; Heller, Alfred

doi:10.1007/978-3-319-43946-4_13

Cited by 36 publications

(20 citation statements)

References 28 publications

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“…C9. Security Intrusion detection ( [8], [15], [52], [74], [76], [77], [143], [148], [209]),false data injection attacks ( [13], [16], [17], [31], [100], [119], [151], [163], [168], [184], [185], [230], [251], [257]), energy theft ( [54], [99], [169], [191], [254], [258]), distinguishing cyber-attacks from physical faults ( [18])…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…[27] Difference between real and Neural networks-ARIMA Supervised predicted consumption Ieracitano et al [34] Statistical features Autoencoder base LSTM Unsupervised Ramchandran et al [36] Raw images and detected edges Convolutional DNN-based Unsupervised autoencoder and LSTM Janetzko et al [46] Power spectrum Clustering and visual analytics Unsupervised Ma el al. [47] Standard deviation of temporal FCD-POD-LSE Unsupervised coefficients Cui and Wang [48] Power consumption time series Polynomial regression and Semi-supervised Gaussian distribution Buzau et al [52] Historic and non-sequential power data DNN-based LSTM and MLP Supervised Lin and Claridge [54] Power consumption deviation between measured Unsupervised and temperature and simulated consumption Araya et al [55] Contextual/behavioral features Detecting incomplete data in Unsupervised sliding window Liu et al [56] Power and temperature Lambda architecture Supervised Current paper Micro-moment consumption and DNN and rule-based algorithm Supervised occupancy data Fig. 1 Block diagram of the proposed system for detecting abnormal energy consumption…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The performance of this scheme is assessed on a real dataset delivered by Powersmiths (Brampton), Ontario, Canada. Authors in [56] develop a supervised learning and statistical-based anomalous power consumption detection system, and use a Lambda scheme that is based on both inmemory distributed computing algorithms, Spark and Spark streaming. Moving forward, a real time anomalous identification is achieved by analyzing scalable live patterns in addition to an iterative process, which helps refreshing consumption signatures from realistic databases.…”

Section: Statistical Techniquesmentioning

confidence: 99%

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

et al. 2020

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Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.

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“…This serving layer will provide the analyzed data to a client who cares about the accuracy of the data and is tolerant regarding the time latency, while for a client who cares about the data speed and is tolerant regarding less accurate data can consume the data directly from the speed layer. The Lambda architecture concept is implemented in practice with different technologies [31].…”

Section: Data Processing Architecture For the Smart Meter Datamentioning

confidence: 99%

Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings

et al. 2018

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Building energy management systems (BEMS) have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data.

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Online Anomaly Energy Consumption Detection Using Lambda Architecture

Cited by 36 publications

References 28 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings

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