4 research outputs found
Smart Meter Development Using Digital Twin Technology for Green Energy Distribution Optimization
This study proposes a digital twin (DT) approach and technical framework for smart meters to solve potential implementation and development problems and adapt to the new energy revolution trend and increase smart grid network security. DT models were deployed in the cloud and edge using a smart meter DT demonstration system. This paper evaluates the DT system's communication performance in real-time smart grid application through three dimensions: remote application service for smart grid user side, P2P transaction on the user side, and user real-time request service. This study's container-based decision tree strategy for smart meters meets the smart grid's real-time communication requirements for user-side applications
Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
Enormous amounts of data are being produced everyday by sub-meters and smart
sensors installed in residential buildings. If leveraged properly, that data
could assist end-users, energy producers and utility companies in detecting
anomalous power consumption and understanding the causes of each anomaly.
Therefore, anomaly detection could stop a minor problem becoming overwhelming.
Moreover, it will aid in better decision-making to reduce wasted energy and
promote sustainable and energy efficient behavior. In this regard, this paper
is an in-depth review of existing anomaly detection frameworks for building
energy consumption based on artificial intelligence. Specifically, an extensive
survey is presented, in which a comprehensive taxonomy is introduced to
classify existing algorithms based on different modules and parameters adopted,
such as machine learning algorithms, feature extraction approaches, anomaly
detection levels, computing platforms and application scenarios. To the best of
the authors' knowledge, this is the first review article that discusses anomaly
detection in building energy consumption. Moving forward, important findings
along with domain-specific problems, difficulties and challenges that remain
unresolved are thoroughly discussed, including the absence of: (i) precise
definitions of anomalous power consumption, (ii) annotated datasets, (iii)
unified metrics to assess the performance of existing solutions, (iv) platforms
for reproducibility and (v) privacy-preservation. Following, insights about
current research trends are discussed to widen the applications and
effectiveness of the anomaly detection technology before deriving future
directions attracting significant attention. This article serves as a
comprehensive reference to understand the current technological progress in
anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table
Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis
The article presents solutions to anomaly detection in network traffic for critical smart metering infrastructure, realized with the use of radio sensory network. The structure of the examined smart meter network and the key security aspects which have influence on the correct performance of an advanced metering infrastructure (possibility of passive and active cyberattacks) are described. An effective and quick anomaly detection method is proposed. At its initial stage, Cookâs distance was used for detection and elimination of outlier observations. So prepared data was used to estimate standard statistical models based on exponential smoothing, that is, Brownâs, Holtâs, and Wintersâ models. To estimate possible fluctuations in forecasts of the implemented models, properly parameterized Bollinger Bands was used. Next, statistical relations between the estimated traffic model and its real variability were examined to detect abnormal behavior, which could indicate a cyberattack attempt. An update procedure of standard models in case there were significant real network traffic fluctuations was also proposed. The choice of optimal parameter values of statistical models was realized as forecast error minimization. The results confirmed efficiency of the presented method and accuracy of choice of the proper statistical model for the analyzed time series