Design and Implementation of LoRaWAN-Based Smart Meter System for Rural Electrification

The Internet of Things will encompass a rich variety of sensing systems including mobile phones, embedded sensor and actuator platforms, and even smart electricity meters. Through their collaborative operation, billions of such devices will realize the vision of smart homes, smart cities, and beyond. Smart electricity meters can, e.g., already help utilities monitor the stability of the power grid by periodically reporting each connected household's energy consumption. Even more sophisticated services can be realized when data is available at higher spatio-temporal resolutions, e.g., when distributed smart power meters (sometimes referred to as smart plugs) are deployed. In this paper, we present one such novel service for the smart home, namely making projections of an appliance's future energy demand based on previously observed power consumption data. In a first step, our system identifies and isolates unique characteristic signatures from collected power consumption traces. Subsequently, time series pattern matching is applied to detect these signatures in real-time data. Based on the occurrences of the extracted signatures in real-time data, the appliance's future power demand is predicted. We evaluate our approach with more than 2,500 appliance activity segments collected from 15 different appliance types, and show that accurate forecasts can be made in many cases.

Smart electricity meter is an important computing terminal in the construction of ubiquitous power Internet of Things, and its reliable life is uneven. The identification of key components of smart electricity meters plays a key role in improving the reliable life of smart electricity meters. This paper presents a method to determine key components of smart electricity meters based on simulation. According to the working principle of smart electricity meters, the key units of smart electricity meters are determined and the simulation model of key units is built. Through the analysis of simulation software, the key components affecting the output characteristics of smart electricity meters are determined, and the failure mechanism of these key components is analyzed combined with the actual working environment of smart electricity meters. This paper provides a reference for the design, production and reliability improvement of smart electricity meters.

Aiming at the problems of high cost, low efficiency, poor accuracy, and incomplete coverage existing in the on-site calibration operation mode of smart electricity meters, a remote estimation method of operation error of smart electricity meters was proposed by analyzing the big data under the advanced measurement system, which took into account the error fluctuation and nonlinear dynamic line loss. Firstly, the fluctuation of smart meter error was analyzed, and the remote estimation model of smart meter operation error was established by screening the electricity quantity data with similar operation states to reduce the influence of error fluctuation in the subsequent model solving. Secondly, the traditional line loss estimation method is improved, and a nonlinear dynamic line loss model is proposed to make the line loss change nonlinearly with the actual power supply so that the error estimate in the model is closer to the actual value. The results show that the proposed method effectively improves the accuracy of the operation error estimation results of smart electricity meters, helps to detect abnormal measurement points timely and accurately, and provides support for the efficient operation and maintenance of smart power grids.

Smart water meters at household connections are being installed in large numbers throughout the world due to the benefits they are expected to bring to the water utilities and water consumers. Smart metering provides high‐resolution readings and promises benefits to the water utilities, such as demand forecasting, regulating time‐of‐use watering, and making intelligent operation and planning decisions. For the consumers, smart metering promises improved billing and demand reduction by providing detailed and timely information about their water use and early notification of possible water leaks in their premises. However, the fine‐grained information collected by smart meters raises growing concerns of privacy invasion due to personal behavior exposure (private activity, daily routine, etc.). Nevertheless, there is no readily available technology for protecting water consumers from revealing their in‐home private activities. Thus, a viable argument in favor of smart metering technologies will not be possible without proactively accounting for the associated privacy challenges. Here, we present a practical technology coupling a dedicated apparatus with a control model for increasing personal privacy. We quantify the level of privacy achieved using information‐theoretic criterion and an empirically based occupancy detection method between the smart meter readings and actual water use. Furthermore, we evaluate and compare privacy protection using the best effort approach previously developed for masking activities revealed from smart electricity meters. The main results reveal that simple control actions can disguise personal behavior patterns and, thus, hedge against privacy breach in smart water meters. Furthermore, we quantify the trade‐off between the size of the apparatus and the level of privacy protection it provides. Our results demonstrate how “privacy friendly” smart water metering technology could be implemented in real‐life systems and reduce the privacy concerns of water consumers.

Smart electric meters collect data on electricity use to potentially improve efficiency for utilities, shape power consumption, and ultimately reduce greenhouse gas emissions. Consumer groups and experts have raised concerns about privacy, as smart meters can provide detailed information about activity within the home. This study examines privacy beliefs of U.S. consumers using qualitative data from focus groups together with experimental data from a national online survey of utility customers. Exploratory analysis of focus group findings suggested that consumers who felt in control of their data perceived fewer risks and more benefits from having access to the data. Participants in the online experiment saw greater risk and less benefit when advised that smart meters could be used to raise prices under time‐of‐use pricing. Participants also saw greater risk when the utility shared customer data with a third party. Risks and benefits influenced perceptions of control. These findings accord with several information privacy frameworks adding new insights about consumers' perceived control over their energy data. The findings can inform utility companies and policymakers with respect to giving consumers more control over their data, reducing consumers' privacy concerns, communicating benefits of smart meter data, and providing accurate information about the risks.

According to the requirements of smart electricity meters test data management service system, on the basis of analyzing the insufficient of the traditional smart electricity meters quality inspection tests. A set of mobile phone APP software and smart electricity meters test data management service system based on the B/ S architecture is developed. A universal data management system platform is designed and implemented. The system architecture, system database and the design ideas of each component module in detail is introduced and introduced the technical tools and methods used in each part. Key technologies such as large data volume database management, data backup and recovery are researched. By partitioning smart electricity meters test data, load data and retrieval test on the system platform. Data management efficiency has been greatly improved compared to traditional methods, which meet the requirements for large data volume data management.

The use of smart meters have been increased in many countries across the globe. Smart meters are not only used for measurement of electricity consumption but also used for the measurement of gas and water consumption. The smart meter is an integral part of the smart grid system and its use incurs benefits to people in various aspects such as social, economic and environmental. The smart meters' main functionality is measuring, capturing and transforming data related to usage or consumption of electricity, gas or water and events such as meter status and power quality. Such functionality encounters many data related issues such as the volume of data, the speed of collecting data and complexity. To gain the hidden insights and knowledge from such data we require the advanced data analytical tools and technologies. This paper provides a brief review of tools and technologies used in smart electricity meter data analytics as well as a comprehensive survey of several key analytical applications and techniques and tools used in those applications.

In order to evaluate the performance of smart electricity meter (SEM), this paper studies the reliability of the SEM. We introduce the indication of the reliability and analyze the standards and documents relevant to SEM. Then, we use the component stress method to predict the reliability of SEM based on the study of SEM. Finally, through an example, we obtain the expected failure rate of SEM from the kind of DDZY102C-Z produced by the Wasion Group through the method proposed. The result of the predicted reliability of the SEM is 5640.43 FIT (failure in time: 10-9Failures/h). Furthermore, in order to validate the proposed method, accelerated test is taken to assess the reliability of the smart electricity meter, and the experimental results show that the predicted reliability of the SEM has a high credibility.

Smart Meters, Personalized Feedback and Retailer Switching in Electricity Markets

Smart electricity meters have been replacing conventional meters worldwide, enabling automated collection of fine-grained (e.g., every 15 minutes or hourly) consumption data. A variety of smart meter analytics algorithms and applications have been proposed, mainly in the smart grid literature. However, the focus has been on what can be done with the data rather than how to do it efficiently. In this article, we examine smart meter analytics from a software performance perspective. First, we design a performance benchmark that includes common smart meter analytics tasks. These include offline feature extraction and model building as well as a framework for online anomaly detection that we propose. Second, since obtaining real smart meter data is difficult due to privacy issues, we present an algorithm for generating large realistic datasets from a small seed of real data. Third, we implement the proposed benchmark using five representative platforms: a traditional numeric computing platform (Matlab), a relational DBMS with a built-in machine learning toolkit (PostgreSQL/MADlib), a main-memory column store (“System C”), and two distributed data processing platforms (Hive and Spark/Spark Streaming). We compare the five platforms in terms of application development effort and performance on a multicore machine as well as a cluster of 16 commodity servers.

The wide application of smart electricity meters is conducive to fine management of electricity consumption and better experience of electricity consumption, but the possibility of users’ privacy being snooped is also gradually increasing. The data security of smart electricity meters has become a priority problem to be solved. Smart meter identity authentication protection is one of the key technologies of smart meter data protection. Identity authentication and digital signature technology based on RSA public key technology provide protection for smart meter identity authentication. Through the transplantation of RSA algorithm to Linux system and performance test, the test analysis shows that RSA algorithm has certain security for data addition. There is no way to be cracked in a short time, and signature verification is faster than signature.KeywordsRSA algorithmIntelligent electric meterComputing timeData security

An in-home display (IHD) is an important add-on to a smart electric meter that provides continuous real-time feedback to households about their energy usage and costs. Utilising experimental variation in an online survey, I estimate a price elasticity of demand for IHD of −0.15, conditional on smart meter adoption. Households who did not pay for their smart meter device are also more sensitive to the IHD price, suggesting potential negative spillovers from smart meter subsidies.

Smart electricity meters are currently deployed in millions of households to collect detailed individual electricity consumption data. Compared with traditional electricity data based on aggregated consumption, smart meter data are much more volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand in order to undertake appropriate planning of generation and distribution. We propose to estimate an additive quantile regression model for a set of quantiles of the future distribution using a boosting procedure. By doing so, we can benefit from flexible and interpretable models, which include an automatic variable selection. We compare our approach with three benchmark methods on both aggregated and disaggregated scales using a smart meter data set collected from 3639 households in Ireland at 30-min intervals over a period of 1.5 years. The empirical results demonstrate that our approach based on quantile regression provides better forecast accuracy for disaggregated demand, while the traditional approach based on a normality assumption (possibly after an appropriate Box-Cox transformation) is a better approximation for aggregated demand. These results are particularly useful since more energy data will become available at the disaggregated level in the future.

For smart electricity meter with multiple performance characteristics (PCs) with coupling relationships because of amounts of components experiencing multiple deteriorating processes, we develop a multivariate degradation modeling method via vine copulas to estimate the reliability of products with multiple PCs reflecting degradation states. In the multivariate model, pair‐copula construction and vine graphical representation are used to describe the mutual relationship among PCs to overcome the lack of multivariate copula in high‐dimensional cases. Each PC model of smart electricity meter is built by using drift Brownian motion to describe degradation processes of each PC on the basis of degradation mechanism analysis, and parameters are estimated by using likelihood estimation method. The Pearson correlation coefficient, Kendall's τ and product information are used to analyze correlation among those PCs. Furthermore, on the basis of conditional probability theory, the vine graph is used to construct a multivariate copula which can be decomposed into pair copulas. Akaike information criterion principle is utilized to choose the forms of pair‐copula functions in the correlation model. Finally, the reliability joint distribution of all PCs of smart electricity meter is obtained with combining all PCs' marginal distributions and copula functions. Copyright © 2016 John Wiley & Sons, Ltd.

In order to solve the problems of heavy workload, weak planning, and repetitive maintenance in the periodic rotation of smart energy meters, the author proposes a verification cycle optimization method based on the evaluation results of energy meter status. This method first obtains data on six indicators of smart energy meters: regional factors, reliability, full event, abnormal metering events, battery overload, and clock battery undervoltage; Subsequently, on the one hand, the coefficient of variation assignment method is used to obtain the status score of each electricity meter, and on the other hand, these six indicator data are used as input data, and the K means clustering algorithm is used to classify and obtain the corresponding categories. Finally, the two algorithms are combined to obtain a new method for evaluating the status of smart energy meters, and the final evaluation result is output. The experimental results indicate that: The number of electricity meters scored below 80 points obtained by this method accounts for 22.08% of the total number of electricity meters, while electricity meters scored above 80 points account for 77.93% of the total number of electricity meters. This indicates that this method is in line with the actual situation and objective laws. Constructing a state evaluation model for electric energy meters, using historical data and on-site calibration data as state variables, analyzing the annual operational quality of electric energy meters, and providing reference basis for adjusting the calibration cycle of electric energy meters.