scholarly journals Thwarting Nonintrusive Occupancy Detection Attacks from Smart Meters

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Dapeng Man ◽  
Wu Yang ◽  
Shichang Xuan ◽  
Xiaojiang Du
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Real World ◽  
Side Effect ◽  
Smart Meter ◽  
Smart Meters ◽  
Occupancy Detection ◽  
Load Monitoring ◽  
Privacy Issues ◽  
Statistical Metrics

Occupancy information is one of the most important privacy issues of a home. Unfortunately, an attacker is able to detect occupancy from smart meter data. The current battery-based load hiding (BLH) methods cannot solve this problem. To thwart occupancy detection attacks, we propose a framework of battery-based schemes to prevent occupancy detection (BPOD). BPOD monitors the power consumption of a home and detects the occupancy in real time. According to the detection result, BPOD modifies those statistical metrics of power consumption, which highly correlate with the occupancy by charging or discharging a battery, creating a delusion that the home is always occupied. We evaluate BPOD in a simulation using several real-world smart meter datasets. Our experiment results show that BPOD effectively prevents the threshold-based and classifier-based occupancy detection attacks. Furthermore, BPOD is also able to prevent nonintrusive appliance load monitoring attacks (NILM) as a side-effect of thwarting detection attacks.

scholarly journals Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4674
Author(s):  
Qingsheng Zhao ◽  
Juwen Mu ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Detection Efficiency ◽  
Evaluation Model ◽  
Smart Meter ◽  
Smart Meters ◽  
Abnormal Detection ◽  
Prediction Residual ◽  
Threshold Setting ◽  
Training Efficiency

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

scholarly journals A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2195
Author(s):  
Hasan Rafiq ◽  
Xiaohan Shi ◽  
Hengxu Zhang ◽  
Huimin Li ◽  
Manesh Kumar Ochani
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Estimation Accuracy ◽  
Post Processing ◽  
Input Space ◽  
Deep Recurrent Neural Network ◽  
Load Monitoring

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

scholarly journals Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5236 ◽  
Author(s):  
Sanket Desai ◽  
Rabei Alhadad ◽  
Abdun Mahmood ◽  
Naveen Chilamkurti ◽  
Seungmin Rho
Keyword(s):  
Power Consumption ◽  
Large Scale ◽  
Single Point ◽  
Ground Truth ◽  
Significant Rise ◽  
The State ◽  
Smart Meters ◽  
Energy Estimation ◽  
Consumption Data ◽  
Load Monitoring

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.

scholarly journals Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5366
Author(s):  
Minzheng Hu ◽  
Shengyu Tao ◽  
Hongtao Fan ◽  
Xinran Li ◽  
Yaojie Sun ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Event Detection ◽  
Essential Feature ◽  
Training Sample ◽  
Identification Accuracy ◽  
K Nearest Neighbors ◽  
Carbon Neutrality ◽  
Energy Management Systems ◽  
Load Monitoring

To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.

scholarly journals Design and Implementation of a Smart Energy Meter Using a LoRa Network in Real Time

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3152
Author(s):  
Francisco Sánchez-Sutil ◽  
Antonio Cano-Ortega ◽  
Jesús C. Hernández
Keyword(s):  
Wireless Networks ◽  
Power Consumption ◽  
Real Time ◽  
Internet Access ◽  
Low Power Consumption ◽  
Smart Meters ◽  
Measuring Device ◽  
Design And Implementation ◽  
Energy Meter ◽  
Residential Electricity

Nowadays, the development, implementation and deployment of smart meters (SMs) is increasing in importance, and its expansion is exponential. The use of SMs in electrical engineering covers a multitude of applications ranging from real-time monitoring to the study of load profiles in homes. The use of wireless technologies has helped this development. Various problems arise in the implementation of SMs, such as coverage, locations without Internet access, etc. LoRa (long range) technology has great coverage and equipment with low power consumption that allows the installation of SMs in all types of locations, including those without Internet access. The objective of this research is to create an SM network under the LoRa specification that solves the problems presented by other wireless networks. For this purpose, a gateway for residential electricity metering networks using LoRa (GREMNL) and an electrical variable measuring device for households using LoRa (EVMDHL) have been created, which allow the development of SM networks with large coverage and low consumption.

Modeling and Simulation of Smart Meters Based on Matlab/Simulink Software

2013 ◽  
Vol 846-847 ◽  
pp. 760-763
Author(s):  
Peng He Zhang ◽  
Cheng Dong Xiao ◽  
Yang Xue ◽  
Xin Lei Zhang
Keyword(s):  
Power Consumption ◽  
Power Grid ◽  
Prediction Method ◽  
Stable Operation ◽  
Smart Meter ◽  
Huge Number ◽  
Smart Meters ◽  
Smart Power ◽  
Matlab Simulink ◽  
Working Principle

Nowadays, smart meters are widely used in power grid, which plays an important role in building the smart power grid and its stability is the key to stable operation of the grid. However, the reliability prediction of the smart meters is difficult to be performed due to its huge number and it cannot be tested one by one. In order to study the prediction method, according to the working principle of smart meters, this paper presents a simulation smart meter model based on Matlab/Simulink software. And related experiments are performed to measure the power consumption under different loads, through comparing the measured power consumption between the simulation model and power analyzer, the correctness of the presented model is verified.

Research on Smart Meter BOA and System Design

2013 ◽  
Vol 397-400 ◽  
pp. 1897-1900
Author(s):  
Jian Yang Zhao ◽  
Jing Mei Cheng ◽  
Wei Hong Ding
Keyword(s):  
Smart Grid ◽  
System Design ◽  
Data Base ◽  
Real Time ◽  
Smart Meter ◽  
Smart Meters ◽  
Operation System ◽  
Linux Operation System ◽  
Advanced Metering ◽  
And Storage

As an important part of the smart grid, smart meters and advanced metering infrastructures are given the newly missions connected network. Along with Ethernet development smart meters with measurements and networks, meter with BOA can become reality. In this paper a system of smart meter BOA and its smart meter networking has developed. It has real time displays and storage of smart meter data. The system uses a ARM9 (S3C2440) chip with a Linux operation system. Gathering from breaker via RS232, Data are sent to BOA server through named pipes to be displayed on web. At the same time, these data are stored in embed data base SQLite, for feature managements.

Real-time day ahead energy management for smart home using machine learning algorithm

2021 ◽  
pp. 1-12
Author(s):  
Nisha Vasudevan ◽  
Vasudevan Venkatraman ◽  
A Ramkumar ◽  
A Sheela
Keyword(s):  
Time Series ◽  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Learning Algorithm ◽  
Mathematical Formulation ◽  
Housing Sector ◽  
Grid Security ◽  
Smart Meters ◽  
Comparison Results

Smart grid is a sophisticated and smart electrical power transmission and distribution network, and it uses advanced information, interaction and control technologies to build up the economy, effectiveness, efficiency and grid security. The accuracy of day-to-day power consumption forecasting models has an important impact on several decisions making, such as fuel purchase scheduling, system security assessment, economic capacity generation scheduling and energy transaction planning. The techniques used for improving the load forecasting accuracy differ in the mathematical formulation as well as the features used in each formulation. Power utilization of the housing sector is an essential component of the overall electricity demand. An accurate forecast of energy consumption in the housing sector is quite relevant in this context. The recent adoption of smart meters makes it easier to access electricity readings at very precise resolutions; this source of available data can, therefore, be used to build predictive models., In this study, the authors have proposed Prophet Forecasting Model (PFM) for the application of forecasting day-ahead power consumption in association with the real-time power consumption time series dataset of a single house connected with smart grid near Paris, France. PFM is a special type of Generalized Additive Model. In this method, the time series power consumption dataset has three components, such as Trend, Seasonal and Holidays. Trend component was modelled by a saturating growth model and a piecewise linear model. Multi seasonal periods and Holidays were modelled with Fourier series. The Power consumption forecasting was done with Autoregressive Integrated Moving Average (ARIMA), Long Short Term Neural Memory Network (LSTM) and PFM. As per the comparison, the improved RMSE, MSE, MAE and RMSLE values of PFM were 0.2395, 0.0574, 0.1848 and 0.2395 respectively. From the comparison results of this study, the proposed method claims that the PFM is better than the other two models in prediction, and the LSTM is in the next position with less error.

scholarly journals Development and Calibration of an Open Source, Low-Cost Power Smart Meter Prototype for PV Household-Prosumers

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 878 ◽  
Author(s):  
Sanchez-Sutil ◽  
Cano-Ortega ◽  
Hernandez ◽  
Rus-Casas
Keyword(s):  
Open Source ◽  
Real Time ◽  
Low Cost ◽  
Easy Access ◽  
International Electrotechnical Commission ◽  
Smart Meter ◽  
Smart Meters ◽  
Accuracy Class ◽  
Real Time Processing ◽  
High Resolution Data

Smart meter roll-out in photovoltaic (PV) household-prosumers provides easy access to granular meter measurements, which enables advanced energy services. The design of these services is based on the training and validation of models. However, this requires temporal high-resolution data for generation/load profiles collected in real-world household facilities. For this purpose, this research developed and successfully calibrated a new prototype for an accurate low-cost On-time Single-Phase Power Smart Meter (OSPPSM), which corresponded to these profiles. This OSPPSM is based on the Arduino open-source electronic platform. Not only can it locally store information, but can also wirelessly send these data to cloud storage in real-time. This paper describes the hardware and software design and its implementation. The experimental results are presented and discussed. The OSPPSM demonstrated that it was capable of in situ real-time processing. Moreover, the OSPPSM was able to meet all of the calibration standard tests in terms of accuracy class 1 (measurement error ≤1%) included in the International Electrotechnical Commission (IEC) standards for smart meters. In addition, the evaluation of the uncertainty of electrical variables is provided within the context of the law of propagation of uncertainty. The approximate cost of the prototype was 60 € from eBay stores.

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