scholarly journals Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenwang Xie ◽  
Leping Zhang ◽  
Bensong Zhang ◽  
Wei Zhang ◽  
Pingping Wang ◽  
...  
Keyword(s):  
Electric Energy ◽  
Measurement Model ◽  
Calculation Model ◽  
Least Square ◽  
Tikhonov Regularization Method ◽  
Smart Meter ◽  
Fusion Model ◽  
Smart Meters ◽  
Analysis Algorithm ◽  
Processing Effect

This work is aimed at solving the morbidity problem of the smart meter fusion model and improve the measurement accuracy and reliability of the smart meter. Starting with the topology of the smart meter, the reason for the serious morbidity of the smart meter model is discussed. First, the basic process of power system state estimation of smart meters is introduced, and the concept of error analysis of smart meters is clarified. Then, the causes and mechanisms of the ill-conditioned problems of the smart meter model are analyzed, and methods to reduce the morbidity of the smart meter calculation model are analyzed. Finally, a data optimization algorithm based on a greedy strategy and an improved Tikhonov regularization method is proposed. The model data is processed and optimized to reduce the morbidity of the smart meter measurement model. The results show that the analysis algorithm for reducing the morbidity error of the smart meter proposed in this study can effectively interfere with the morbidity of the smart meter calculation model. The processing effect shows that it can reduce the measurement error of the smart meter to about 5%, which is an order of magnitude lower than the error before processing, and the processing effect of the least square method is improved by more than 70%. From the perspective of processing speed, when the user number is between 50 and 100, the running time of the algorithm ranges between 1.5 and 3.5 s, which can be fully adapted to the actual situation and has strong practicability. In short, this study is helpful in improving the accuracy and reliability of smart meter calculations and provides a certain reference for related research.

scholarly journals Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1539
Author(s):  
Yu-Chen Hu ◽  
Yu-Hsiu Lin ◽  
Harinahalli Lokesh Gururaj
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Smart Meter ◽  
Energy Decomposition ◽  
Smart Meters ◽  
Electrical Grid ◽  
Electric Energy Consumption ◽  
Partitional Clustering ◽  
Neuro Fuzzy ◽  
Consumption Data

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

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.

The Pose of Data Model and Research Based on Analysis of Intelligent Test Paper

2013 ◽  
Vol 303-306 ◽  
pp. 2203-2206
Author(s):  
Cai Li Fang ◽  
Lin Wu
Keyword(s):  
Data Model ◽  
Paper Analysis ◽  
Calculation Model ◽  
Test Paper ◽  
Fast Reaction ◽  
Analysis Algorithm ◽  
The Future ◽  
Education Measurement ◽  
Calculation Models

As a important part of teaching, Traditional paper analysis algorithm is complicated and not comprehensive enough. This paper according to the indicators of education measurement and improves the traditional calculation models, the system assesses the perfect calculation model, The teacher also can get the true and fast reaction to students' knowledge and the quality of the paper. This method thereby provides a convincing basis for the improvement on the future teaching and increase the effectiveness in teaching

AI and privacy concerns: a smart meter case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jillian Carmody ◽  
Samir Shringarpure ◽  
Gerhard Van de Venter
Keyword(s):  
Private Information ◽  
Personal Information ◽  
Consumer Information ◽  
Data Sets ◽  
Smart Meter ◽  
Smart Meters ◽  
Content Type ◽  
Privacy Concerns ◽  
Privacy Legislation

Purpose The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection. Design/methodology/approach The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies. Findings The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets. Social implications The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions. Originality/value The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.

scholarly journals Fraud risk and trust on the intention to buy of e-commerce

2021 ◽  
Vol 3 (1) ◽  
pp. 45-52
Author(s):  
Dewi Anggun Puspitarini ◽  
Prawira Aros Purnama ◽  
Isti Riana Dewi
Keyword(s):  
Purchase Intention ◽  
Measurement Model ◽  
Purchase Intentions ◽  
Partial Least Square ◽  
Least Square ◽  
Fraud Risk ◽  
Inner Model ◽  
Deceptive Behavior ◽  
The Government ◽  
The Relationship

This study aims to analyze and obtain empirical evidence of deceptive behavior as a moderating variable of trust in purchasing through e-commerce purchase intentions. The data of this research were obtained from the results of collecting a questionnaire of 100 respondents who were consumers who had made transactions through e-commerce sites. Testing the hypothesis in this study using the measurement model (Outer Model) and structural models (Inner Model) with the application of Partial Least Square (PLS). The PLS program used is SmartPLS version 3.02.8. The results of this study indicate that trust has a positive effect on purchase intentions. However, with deceptive behavior as moderation, deceptive behavior weakens the relationship between trust in purchasing through E-Commerce and purchase intention as a mediating variable. Purchase intention is proven to be a mediating variable that mediates trust in mentally buying. The government should be more assertive in handling cases of e-commerce crime that are rife to create security and comfort for e-commerce site users.

scholarly journals Electric Consumption Assessment using Smart Meter Data and KPI Methodology

2020 ◽  
Vol 57 (3) ◽  
pp. 3-19
Author(s):  
A. Mutule ◽  
I. Zikmanis ◽  
A.-M. Dumitrescu
Keyword(s):  
State Of The Art ◽  
Residential Area ◽  
Electric Power System ◽  
Modern World ◽  
Specific Situation ◽  
Smart Meter ◽  
Smart Meters ◽  
The World ◽  
Study Case ◽  
Grid Operators

AbstractIn the modern world, many cities make use of state-of-the-art technologies for a diversity of applications. A field with very specific needs is the electric power system that deals with both large entities that govern themselves (grid operators) and the citizens. For both and all actors in between, there is an increased need for information. Steps to provide these data are always taken and several initiatives are ongoing across the world to equip residential users with last generation smart meters. However, a full deployment is still not possible. Considering this aspect, the authors propose KPIs for the specific situation when some information is available from the meters and other sources, but some is not. The study case is based on a residential area occupied mainly by university students and after an extensive measurement campaign the results have been studied and analysis methods proposed.

scholarly journals Mobile Application for Electric Power Monitoring on Energy Consumptions at a Campus University

2018 ◽  
Vol 11 (2) ◽  
pp. 637
Author(s):  
Murizah Kassim ◽  
Maisarah Abdul Rahman ◽  
Cik Ku Haroswati Che Ku Yahya ◽  
Azlina Idris
Keyword(s):  
Energy Consumption ◽  
Electric Power ◽  
Mobile Applications ◽  
Working Hours ◽  
Mobile App ◽  
Smart Meter ◽  
University Campus ◽  
Smart Meters ◽  
Power Monitoring ◽  
Energy Consumptions

This paper presents a research on electric power monitoring prototype mobile applications development on energy consumptions in a university campus. Electric power energy consumptions always are the issue of monitoring usage especially in a broad environment. University campus faces high used of electric power, thus crucial analysis on cause of the usage is needed. This research aims to analyses electric power usage in a university campus where implemented of few smart meters is installed to monitor five main buildings in a campus university. A Monitoring system is established in collecting electric power usage from the smart meters. Data from the smart meter then is analyzed based on energy consume on 5 buildings. Results presents graph on the power energy consume and presented on mobile applications using Live Code coding. The methodology involved the setup of the smart meters, monitoring and data collected from main smart meters, analyzed electrical consumptions for 5 buildings and mobile system development to monitor. A Live Code mobile app is designed then data collected from smart meter using ION software is published in graphs. Results presents the energy consumed for 5 building during day and night. Details on maximum and minimum energy consumption presented that show load of energy used in the campus. Result present Tower 1 saved most eenergy at night which is 65% compared to block 3 which is 8% saved energy although block 3 presents the lowest energy consumption in the working hours and non-working hours. This project is significant that can help campus facility to monitor electric power used thus able to control possible results in future implementations.

scholarly journals Wireless Home Energy Meter Manager using Android: A Preliminary study

2019 ◽  
Vol 18 (3-2) ◽  
pp. 32-36
Author(s):  
Sh. Nurul Hidayah Wan Julihi ◽  
Ili Najaa Aimi Mohd Nordin ◽  
Muhammad Rusydi Muhammad Razif ◽  
Amar Faiz Zainal Abidin
Keyword(s):  
Energy Consumption ◽  
Personal Information ◽  
Current Sensor ◽  
Smart Meter ◽  
Smart Meters ◽  
Long Distance ◽  
Energy Meter ◽  
Utility Companies ◽  
Preliminary Study ◽  
To Receive

Manual home energy meter reading and billing had caused inconvenience to the utility companies due to lack of manpower to read the energy meter at each household especially in the remote area, explains the increasing number of smart meter reader in the current market. Most of the smart meters in the market do not offer safety of privacy of consumers’ personal information since the data of electricity usage is being transferred digitally to the utility companies for more accurate bills calculation. Plus, the smart meter system is also a bit pricey to be installed in the rural area. Therefore, a private system that able to read energy consumption from a DC load and calculate its bill according to the tariff is proposed. Value of current is being obtained by using ACS712 current sensor. Hall circuit in the current sensor will converts magnetic field into a proportional voltage. The proposed system allows energy meter monitoring from an Android-based smartphone by displaying the real-time energy consumption and bill on Blynk application. An interface of Blynk is developed and connected to WiFi module, ESP8266 for visualizing the energy consumption of the DC load. In conclusion, the Energy Meter transmitter part able to read, calculate and transmit value of energy consumption and current bills to the Blynk application and Blynk application able to receive and show all the data transmitted at the present time. This system will be further improved for long-distance monitoring of electrical appliances used at home.

Smart Metering for Applications

2014 ◽  
Vol 960-961 ◽  
pp. 823-827
Author(s):  
Ying Pan ◽  
Bo Jiang
Keyword(s):  
Smart Grid ◽  
Electrical Energy ◽  
Smart Meter ◽  
Smart Metering ◽  
Power Network ◽  
Energy Usage ◽  
Smart Meters ◽  
Additional Information ◽  
Energy Efficiency Improvement ◽  
Energy Meter

As an important part of Smart Grid, smart metering attracts more and more attention all over the world. It is the way for energy consumer to sense the benefit of smart grid directly. Smart meter is an advanced energy meter that measures consumption of electrical energy providing additional information compared to a conventional energy meter. This paper discusses various applications and technologies that can be integrated with a smart meter. Smart meters can be used not only from the supply side monitoring but also for the demand side management as well. It plays an important role to monitor the performance and the energy usage of the grid loadings and power quality. In addition, This paper gives a comprehensive view on the benefit of smart metering in power network such as energy efficiency improvement.

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