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The introduction to this theme issue discusses a series of papers examining the increasing marketisation of social reproduction and its effects on systems that sustain human and social life. This is done by examining the frontiers, framings, and frictions that arise when market systems are constructed to enable capital accumulation in the realm of social reproduction. Frontiers identify the expansion of market logic into new areas, framings explore how financial actors attempt to bring the logic of social reproduction within the purview of market competition, and frictions highlight the various tensions that generate resistance to the roll out of market logics. Through establishing these three areas, we argue that both market structures and systems of social reproduction should be understood as geographically variegated and, at times, uncertain. This variegation necessitates an understanding of marketised social reproduction as forged through complex articulations of market and non-market logics. Using cases from surrogacy to smart electricity meters, the papers in this theme issue illustrate that while these articulations may generate benefits for some individuals, households and communities, such processes of marketisation can introduce new layers of inequity and undermine the ethical relations and social commitments that sustain life—in the service of enabling accumulation. [ABSTRACT FROM AUTHOR]

This study examines the quantitative conditions under which an energy metering pricing model is proposed to increase both gas merchant profits and gas customer consumer surplus compared to a volumetric pricing model. The quantitative condition is found to be related to factors such as the standard unit calorific value of natural gas prescribed by the National Development and Reform Commission (NDRC) and other relevant government departments under the energy metering pricing model. This paper establishes a mathematical model based on optimization theory to explore the operational decisions of city gas suppliers in the volumetric and energy metering and pricing modes, respectively, under the condition of relatively stable natural gas sales price. The results of the study show that DAC and other authorities can regulate the standard unit calorific value of natural gas under the energy metering and pricing model by regulating the standard unit calorific value of natural gas. This affects the incentives of gas dealers to produce and operate, guides the preference of gas users for natural gas energy metering and pricing, and results in the derivation of formulas for a reasonable range of standard unit calorific values for natural gas. The findings of this paper provide theoretical support to promote the reform of natural gas energy measurement and pricing, and contribute to the development of the natural gas industry. [ABSTRACT FROM AUTHOR]

Smart meters (SM) arrangement play an essential role in Smart Grid (SG), serving as advanced energy meters crucial for monitoring and managing energy consumption. This paper provides an in-depth exploration of the significance, functions, and technological aspects of smart meters within the context of the SG. Smart meters acquire data from endusers' load devices, offering a comprehensive measurement of consumers' energy consumption. The obtained information is then communicated to utility companies and system operators, facilitating efficient energy management. The integration of control devices and a number of sensors, supported by a dedicated communication infrastructure, enhances the reliability and functionality of smart meters. The paper summarizes hardware model key facets and functions of smart meters, offering a comprehensive view of their capabilities. [ABSTRACT FROM AUTHOR]

In order to effectively utilize the large amount of high-dimensionality historical data generated by energy meters during operation, this paper proposes a DBN-MLP fusion neural network method for multi-dimensional analysis and fault-type diagnosis of smart energy meter fault data. In this paper, we first use DBN to strengthen the feature extraction ability of the network and solve the problem of many kinds of feature data and high dimensionality of historical data. After that, the processed feature information is input into the MLP neural network, and the strong processing ability of MLP for nonlinear numbers is used to solve the problem of weak correlation among data in the historical data set and improve the accuracy rate of faults diagnosis. The final results show that the DBN-MLP method used in this paper can effectively reduce the number of training iterations to reduce the training time and improve the accuracy of diagnosis. [ABSTRACT FROM AUTHOR]

Energy efficiency is an important issue in industry, especially with the ever-increasing consumption of electrical energy. The power quality and the traceability of metering devices are essential when integrating energy metering systems for energy efficiency. This management requires an understanding of electrical current events such as pulse and transient currents. Current transducers are widely used to measure these electrical current events up to a few megahertz. Their use makes it possible to measure not only the main current flowing through the transducer, but also the bypass current that affects electrical equipment. Calibration of these sensors up to a few megahertz then becomes an essential step. Currently, most calibration methods are limited to 100 kHz frequency for a current of 10 A. This paper presents an improvement of a traceable calibration methodology for current transducers up to 10 A and 1 MHz, thus increasing, by 10 times, the current level for such high frequency applications. This calibration methodology is based on a metrological traceability chain (uninterrupted link to the International System of Units) with respect to a calculable current shunt and is currently the only traceable method for calibrating current transducers at 10 A and up to 1 MHz. The uncertainty obtained for the transimpedance ratio is less than 0.2%, which is considerably reduced with respect to the existing capabilities. [ABSTRACT FROM AUTHOR]

In the European Union, increasing attention is already being paid not only to the development of renewable energy sources, but also to the establishment of solutions to achieve local energy self-sufficiency while increasing the role of citizens in managing the energy they generate. This approach is expected both to have a positive impact on the environment and the reduction of greenhouse gas emissions, and to enhance energy security—both in economic and civic terms by, i.a., combating energy poverty. The development of local energy communities promoted in the EU is supported i.a. by energy cooperatives. These contribute to the efficient harnessing of renewable energy potential in rural and urban-rural areas, and have been developing in Poland for several years now. In their previous studies, the authors of this research paper attempted to verify the generation (number, type and capacity of installed sources) and consumption (energy demand) configurations in which an energy cooperative would be a viable solution for prosumers who might establish it. However, over the past few years, the conditions for prosumers and the method of their accounting with the electricity seller have changed radically in Poland (shift from net metering to net billing). This situation has opened up space for further research and encouraged the authors to revisit the problem of analyzing the viability of establishing energy cooperatives in relation to the rules of operation of individual prosumers. This research was carried out for three scenarios, and the horizon of the analyses conducted and described extends to 2045. The comparative analysis included energy consumers without their own generation sources, prosumers with a photovoltaic generation installation covered by the net billing model, as well as a scenario involving prosumers' cooperation within an energy cooperative, which by law is settled in the net metering model. Conclusions from the research and simulations made it possible to confirm the claim that, despite changes in the rules of prosumer billing, developing energy independence in the energy community formula results in a significant reduction in the cost of purchasing electricity (even several times lower purchase costs in the timeframe analyzed) and can lead to a reduction in the payback time of investments in generation sources even by a factor of two. The results presented in this research paper open up space for further research. The outcomes allow us to assume that energy cooperatives—in the organizational and institutional model in Poland—are a good tool for reducing the phenomenon of energy poverty on a local scale. [ABSTRACT FROM AUTHOR]

Due to the influence of the connection distance between the mutual inductor primary circuit and the metering device secondary circuit, the detection induced current of the traditional method is extremely small, which cannot accurately detect the authentic situation of the wiring, resulting in long detection time and low detection accuracy. Therefore, this paper proposes the automatic detection method of the secondary circuit connection of the metering device mutual inductor. Through the voltage sampling circuit, the secondary circuit measurement data are obtained. Using the pulse signal shaping function and the ADE7753 chip to process the secondary circuit measurement data, the secondary circuit connection fault judgment is determined. According to the judgment, the electric energy meter is used, combined with the secondary circuit measurement data, and the automatic detection of the secondary circuit connection is achieved by calculation. The experimental results show that compared with the traditional detection method, the automatic detection method of the secondary circuit connection of the metering device mutual inductor designed in this paper achieves normal and stable detection induction current. There is no inductive current with an extremely small condition, the detection accuracy is high, and the detection time is short, indicating that the method is suitable for application in actual detection projects. [ABSTRACT FROM AUTHOR]

Iraqi people have been without energy for nearly two decades, even though their geographic position provides a high intensity of radiation appropriate for the construction of solar plants capable of producing significant quantities of electricity. Also, the annual sunny hours in Iraq are between 3,600 to 4,300 hours which makes it perfect to use the photovoltaics arrays to generate electricity with very high efficiency compared to many countries, especially in Europe. This paper shows the amount of electric energy generated by the meter square of crystalline silicon in the photovoltaic (PV) array that already installed in 18 states in Iraq for each month of the year. The results of the meter-square of PV array in three tracking positions are presented in this paper. This paper shows that the average electricity generated in North cities (Dohuk, Al-Sulaymaniyah, and Erbil) are less than the southern cities in the winter season (three positions) by about 40-50%. Iraq has a stable PV electrical generation during all the year in all regions except the North cities while the highest cities in electricity generation are (Najaf and Al-Anbar). [ABSTRACT FROM AUTHOR]

This paper presents the development, design, and implementation of a SCADA monitoring power meter system for RF and IoT networks. The system collects, measures, stores, and visualizes energy data such as voltage, current, power, energy, reactive power, reactive energy, and power factor, as well as appropriate process data in two remote industrial plants. The first energy measuring point is the MASTER station and it is in the first plant, while the second energy measuring point is the SLAVE station and it is in the second plant. The SCADA monitoring system is built into the MASTER station, and the SLAVE station is connected to it by an RF radio frequency connection. Both stations contain microcomputers, energy measurement units, and appropriate RF modules. The microcomputer in the MASTER station is serially connected to the IoT microcomputer for the transfer of measurement data in the Internet network. This solution provides visualization, data log files, and transfer to the IoT network of the energy and process data from the two measuring points. [ABSTRACT FROM AUTHOR]

Traditionally, reading of energy meters is done by human operators who move from house to house collecting readings. This exercise requires huge labor operator numbers and a lot of time to achieve complete reading of meters in a particular area. This paper endeavors to do away with this problem by using a telemetering system. With this system, power can be measured from a remote site. To achieve this, readings from an energy meter on site are processed by a microcontroller device that then sends the energy consumption information to a Global System Mobile (GSM) module. The GSM module then sends this information using microwaves to a particular mobile subscriber who receives it in the form of an Short Message Service (SMS). The reading is also transmitted to a web based telemetering system through a server. In the remote site, a remote computer which can access the internet is able to obtain data from the server through the internet. The microcontroller is developed in such a way that it achieves communication between the energy meter and the particular server using Transmission Control Protocol(TCP) protocol. Software programs are also developed to facilitate transfer of data to the user at remote site. With the web based telemetering system connected to the internet, electrical energy measurements can be accessed globally. The efficiency of the transmission channel is important for the success of both SMS and web based telemetering systems. [ABSTRACT FROM AUTHOR]

Featured Application: Improving the measurement of electricity consumption by reducing errors is the main objective of this article. As a concrete application, the presented algorithm is used for the determination of electricity meters by the end user. With minimal costs, a correct assessment of energy consumption can be made, and therefore the calculated electricity costs should be accurate. Although data acquisition is a very usual technique, several aspects are not always considered, such as the synchronization of the acquired measures and the evaluation of the resulting errors. This paper aims to highlight this fact by the mathematical determination of the necessary correction and the implementation of software meant to evaluate the performances of acquisition systems. As an example, a three-phased acquisition system was developed in order to monitor the currents and voltages on the three phases. Also, other measures were performed, such as of power and phase. The components on each phase did not have to be fully identified because a whole system calibration could be performed in the first stage. The calibration consisted in finding the weighting coefficients for each measured quantity. The implemented solution for three-phased measure acquisition started from the hypothesis of a sampling frequency that respected the Shannon theorem. The distance between two samples was small enough to consider a linear evolution between two moments for the same measure. Errors that affected the above-mentioned measures, due to the fact that the samples were examined in different moments, were analyzed and brought to the minimum value. Finding a solution to reduce the sampling errors is closely related to reducing the costs. [ABSTRACT FROM AUTHOR]

With the rapid development of the information age, smart meters play an important role in the smart grid. However, there are more and more attacks on smart meters, which mainly focus on the identity authentication of smart meters and the security protection of electricity consumption data. In this paper, an efficient lightweight smart meter authentication scheme is proposed based on the Chinese Remainder Theorem (CRT), which can realize the revocation of a single smart meter user by publishing a secret random value bound to the smart meter identity. The proposed scheme not only protects the security of smart meter electricity consumption data by using encryption, but also resists identity attacks from both internal and external adversaries by using hash functions and timestamps. Experiment shows that the proposed scheme has lower computation overhead and communication overhead than other authentication schemes and is more suitable for smart meter authentication. [ABSTRACT FROM AUTHOR]

Faced with the pressure of energy conservation and emission reduction, the power industry is urgently requires low-carbon transformation. The carbon flow calculation theory redistributes the actual carbon generated by the power plant to the branch and loads customers with the power flow. This paper first introduces the theory of carbon flow calculation and the carbon metrics corresponding to the electricity metrics. Second, a time-synchronous technology is introduced for the carbon flow calculation of transmission, transformation, and distribution networks, and a time-synchronous-based carbon metering system is conceived. The impact of time deviation on carbon metering is elucidated through simulation experiments of IEEE14 standard nodes, and finally, relevant suggestions are made for future research ideas and technical routes. [ABSTRACT FROM AUTHOR]

A monitoring system is needed to determine the performance of an off-grid solar power plant. This paper presents a monitoring system design for an off-grid solar power plant with a capacity of 4 kWp. This off-grid solar power plant system was built on campus 2 of Institut Teknologi Nasional Malang. The purpose of a monitoring system is to collect and present information systematically about the state of the off-grid solar power plant system. This information can be accessed anywhere and anytime by users. Hardware used includes SPM91 to read energy meters digitally, PZEM-017 as a Modbus-based measuring instrument, and communication devices to send data to a database server. The monitoring system dashboard was built using Haiwell SCADA software. The result was a monitoring system that can display parameter data, data loggers, and graphs of the condition of the off-grid solar power plant. The PZEM-017 has an accuracy rate of 99.48% for voltage readings and 98.76% for current readings. Meanwhile, the SPM91 has an accuracy rate of 99.89% for voltage readings and 99.25% for current readings. [ABSTRACT FROM AUTHOR]

Metrological characterisation of static energy meters under realistic low power quality conditions is a basic requirement for proper grid control and fair energy billing. The paper reports about a new proposed methodology, where the meters are tested under conditions directly recorded at installation sites. The waveforms of voltages and currents are sampled using a portable instrument; they are reproduced in laboratory conditions with a phantom power generator, with a bandwidth covering up to the 40th harmonic. The recording site is a photovoltaic energy production facility, having a a nominal power of 50 kW, at the coupling section to the grid. These waveforms were then reproduced in the laboratory, and tested on different models of single- and three-phase commercial static energy meters; the models chosen represent both energy meters used by energy providers at the point of common-coupling, and also meters typically used for in-line monitoring by end users. The quantity of interest is the reading error of the measured energy, when tested with the conditions reproduced from the on-field measurements, in comparison with a reference meter. All tested energy meter models comply with the present international documentary standards, which require tests under low power quality conditions; nevertheless, there are models that show unacceptable errors (up to 25%) in the measurement of active energy when tested with the on-field recorded waveforms. This suggests that the standardised testing waveforms might, in some cases, be not fully representative of the actual conditions encountered in the field. [ABSTRACT FROM AUTHOR]

The opening up of the railway to competition in Europe has been accompanied by many technical challenges never before seen in the railway field. One of these challenges concerns precise energy metering. As European railways have been a monopoly until now, the current systems can only measure the energy consumption of a single operator. In accordance with European law, the installation of an energy meter has been mandatory in new or refurbished rolling stocks since 2014. Nevertheless, these energy meters are still not fully reliable, with misreadings and missing readings being common occurrences. To address the problem of energy metering data reliability, this paper presents a method for validating the reading measurement based on a physical model and validates it in two different case studies. For the issue of energy metering data availability, different estimators are developed, and their performance are shown with the aim of calculating the missing consumptions for both case studies. Finally, the paper suggests how these methods may be implemented in other types of trains. [ABSTRACT FROM AUTHOR]

This paper deals with an energy metering system for electricity monitoring for each and every household that is different from the conventional energy meter. This proposed design is meant to be a better alternative for an efficient and more secure line for energy usage by the consumer without any illegal access to their connections so that they don't have to pay for something they aren't accountable for in the first place and protect the supplier/provider, which is the government, from incurring any losses from the theft as well as from customers who tamper with the meter values. These things are vital in today's world. So, we are taking this into account and designing a smart meter with the IoT technology for monitoring data values at the tip of our fingers, readily available at any moment. It serves many purposes, from the monitoring of energy consumption to easily using mobile phones to work on managing the energy usage according to their budget. It is also easy for the supplier to monitor without manually inspecting the location. The WiFi module ESP8266 is used for the purpose of transmitting the energy data to the server. This paper also deals with a smart grid system in which energy meters are used as a two-way communication system as opposed to conventional meters with one-way communication, which will be a more efficient system. This IoT is good in terms of its development towards an easy and advanced metering infrastructure, which also needs secure communication between the supplier and consumer to protect that data. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor network that operates underwater, typically in oceans, lakes, and rivers. UWSNs are composed of a large number of small sensor nodes that are equipped with various sensing and communication capabilities. These nodes are deployed in the underwater environment to collect and transmit data, which can be used for a variety of applications such as environmental monitoring, oceanography, and marine biology. The Underwater WSN (UWSN) consists of sensor nodes to sense the data and transmit it to the sink node. These sensor nodes (SN) are equipped with limited batteries, which is the central issue. Therefore, the routing protocols were developed for researchers to save energy. However, the increment of network lifetime remains an open challenge. Forwarding the data to the nearest SN to the sink will reduce the network reliability and stability, draining SN's energy early. To overcome these issues, this paper focused on developing an efficient Cooperative based routing (CR) with a machine learning (ML) model to improve the network's lifetime. The cooperative routing discovers the route path from the sender to the destination. The best possible way from the sender to the receiver has been selected using the ML approach called the Self-organizing network (SON). By identifying congestion-free multi-hop transmission using CRSON, the data packet is transmitted from sender to receiver with reduced energy, increasing the network's lifetime and reliability. This model is simulated and experimented with energy efficiency, packet delivery, loss rate, latency, and throughput metrics. [ABSTRACT FROM AUTHOR]

This paper offers affordable controlled charging and a vehicle-to-grid solution that uses a non-smart power meter. Additional communication infrastructure in the low-voltage distribution network is not needed. Ladder logic control is developed to be easily implemented by any commercial programmable logic controller. The voltage values measured by the power meter and electric vehicle battery state of charge are the decision variables. A validity check based on the Monte Carlo method is conducted to test the effectiveness of the proposed solution. Electric vehicle charging requirements are simulated based on travel surveys. The results demonstrate the effectiveness of the proposed solution. The voltage level is kept within acceptable limits according to the power quality standard EN50160. One may argue that there is a mismatch between the rate of penetration of electric vehicles and the level of smart grid implementation, especially in developing countries with higher rates of wealth inequality. Therefore, the methodology presented in this paper can be seen as an affordable mid-way solution that will help distribution system operators in developing countries prepare for higher penetration levels of EVs. [ABSTRACT FROM AUTHOR]

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The informatization construction of the power grid is becoming increasingly popular, business application systems are constantly emerging, and power-related data is rapidly expanding. These discrete power data are scattered in various application systems, and it is not easy to directly provide advanced enterprise applications. The establishment of intelligent power statistical model is an urgent need for constructing power grid informatization. This paper proposes a model of an electric energy metering system based on intelligent sensor data and introduces the existing digital metering system. This model is the integration and promotion of business integration based on the digital metering system. It is the first time to apply new metering equipment, such as measurement and control devices with integrated metering functions, and new metering technologies, such as IEC 61850 electricity meter reading applications. It is hoped that this paper can lay a foundation for further research. [ABSTRACT FROM AUTHOR]

Initial and maintenance costs often prevent dense submeter installations that enable room-level thermal energy monitoring. Previous studies suggested that building automation system (BAS) trend data represents an untapped potential to disaggregate existing meter data for heating and cooling into device- and system-level end-uses. These techniques disaggregate meter data by analyzing trend data that provide contextual information regarding the operating status of energy-consuming equipment. However, the level of submetering required to enable end-use disaggregation has yet to be studied. To this end, this paper investigates the effect of submeter density and configuration on the performance of a regression-based disaggregation strategy using BAS trend data as predictors. The method was evaluated in two steps; first, using synthetic meter and BAS trend data generated by a building performance simulation (BPS) model of a government office building, and second, with submeter data from a real office building. The results highlight the factors affecting the minimum number of heating energy submeters needed to be installed in both buildings for accurate device- and system-level disaggregation. The methodology presented in the paper can also inform changes in building design codes and standards regarding the minimum density and appropriate configuration for submetering. [ABSTRACT FROM AUTHOR]

Energy is one of the essential inputs for modernization and social development. Energy demand is increasing, and the primary energy source is fossil fuels, which negatively impact the environment. Energy saving and renewables are the potential solutions which could minimize environmental impact. This paper investigates the energy-saving and solar photovoltaic energy potential of an educational institution, Politeknik Sultan Azlan Shah (PSAS), Malaysia. The feasibility analysis was conducted by assuming that PSAS joined the Net Energy Metering (NEM 3.0) program, where PSAS, as a NEM consumer, has a tripartite supply agreement with renewable energy (SARE) with a distribution licensee known as Tenaga National Berhad (TNB). This paper focuses on zero capital expenditure (CAPEX) saving through a 20-year contract. This paper proposes a rooftop solar photovoltaic diagram using a NEM meter installed in the ring distribution system at PSAS. The estimated savings to be obtained by PSAS in the 20 years that the contract is in force are calculated based on the assumption that the installed solar system has a capacity of 688 kW. The maximum value of power generated by the system for a year is 990,720 kWh. The feasibility analysis found that the cumulative net savings estimate for PSAS based on the overall calculation for 21 years of solar use is RM 3,534,250. Meanwhile, the cumulative assessment of carbon emission reduction obtained in the same period is 14,559,760 kg CO2 or 14,559.76 tons of CO2, which would save 363,994 mature trees from being cut down. [ABSTRACT FROM AUTHOR]

Summary: Many electricity consumers around the world are billed electric peak load charges. In the United States, these demand charges can contribute up to 70% of a consumer's electricity bill. Research has shown that the financial impact of these charges to the consumers can be reduced by acting on the intelligence provided by peak electric load day (PELD) forecasts. Unfortunately, published research detailing PELDs forecasting methodologies for facilities adopting behind the meter renewable electricity generation (BTMREG) is non‐existent. This paper contributes: (a) a PELD forecasting methodology applicable to consumers with BTMREG. A case study demonstrates the methodology's ability to achieve 93% of the potential savings in kW, translating to demand charge reductions of approximately US$ 142 129 per year. (b) The first side‐by‐side performance comparisons of electric load and PELD forecasting models for scenarios with and without BTMREG. (c) The first PELD forecasting model savings comparison for scenarios with and without BTMREG. Experimental results suggest that counterintuitively, BTMREG adoption can translate into higher peak load savings for electricity consumers. [ABSTRACT FROM AUTHOR]

The growing adoption of renewable energy sources and the need for more efficient and secure energy grids are revolutionizing the energy sector. Electricity monitoring becomes an issue of utmost importance, as current traditional energy meters have several problems in terms of lack of transparency, very high operational costs, and the possibility of being easily tampered with. This paper proposes a new system for electricity production metering that leverages blockchain and IoT for decentralized and secure data recording while protecting user privacy and reducing operational costs. The architecture results in improvements over the traditional energy meter. The system also contributes to the generation of big data that is reliable, traceable, error-proof, and highly resistant to cyber attacks. The architectural project outputs are a smart energy meter, a smart contract on the Ethereum blockchain, and a decentralized application to manage the information recording. The experimental prototype outcomes confirm the use of these new technologies to improve energy metering, enhancing efficiency, transparency, and traceability, with reduced costs and increased user privacy. [ABSTRACT FROM AUTHOR]

Traditional energy meters have suffered from a lack of automated analysis and inaccuracy in reading energy consumption, which has brought about smart metering systems. Developing economies such as in Africa. still experience a setback in electricity monitoring and load distribution because of existing traditional meter systems in use. Communication technologies play an important role to improve the monitoring of energy consumption and ensure a road map toward a smart grid. This paper reviews communication technologies used for Advanced Metering Infrastructure (AMI) emphasizing Wi-Fi and Cellular technologies. Metrics used to evaluate their performance include cost, energy efficiency, coverage, deployment, latency, payload, and scalability. The review presents a benchmark for research on AMI communication technologies in developing economies. When adopted, the expected AMI benefits are reduced energy theft, cost efficiency, real-time analysis, security, and safety of energy supply in developing economies. [ABSTRACT FROM AUTHOR]

Benford's law can be used as a method to detect non-natural changes in data sets with certain properties; in our case, the dataset was collected from electricity metering devices. In this paper, we present a theoretical background behind this law. We applied Benford's law first digit probability distribution test for electricity metering data sets acquired from smart electricity meters, i.e., the natural data of electricity consumption acquired during a specific time interval. We present the results of Benford's law distribution for an original measured dataset with no artificial intervention and a set of results for different kinds of affected datasets created by simulated artificial intervention. Comparing these two dataset types with each other and with the theoretical probability distribution provided us the proof that with this kind of data, Benford's law can be applied and that it can extract the dataset's artificial manipulation markers. As presented in the results part of the article, non-affected datasets mostly have a deviation from BL theoretical probability values below 10%, rarely between 10% and 20%. On the other side, simulated affected datasets show deviations mostly above 20%, often approximately 70%, but rarely lower than 20%, and this only in the case of affecting a small part of the original dataset (10%), which represents only a small magnitude of intervention. [ABSTRACT FROM AUTHOR]

The charging load forecasting of residential Electric Vehicles help grid operators make informed decisions in terms of scheduling and managing demand response. The residence can include integrated residential appliances with multi‐state and high‐frequency features. For this reason, it is difficult to estimate the total load of residence accurately. To overcome this problem, this paper proposes a hybrid forecasting model using the empirical mode decomposition and Bayesian optimised Long Short‐Term Memory for load balancing based on residential electricity meter data. The residential electricity meter data includes three datasets as Electric Vehicle, heat pump and photovoltaic system. To decompose of the data characteristics, the empirical mode decomposition method performs to the original data. Then, the Bayesian optimised Long Short‐Term Memory is applied to forecast for each sub‐component of the data sequentially. The main features of the proposed model include a significant improvement in prediction accuracy and capture the local maximums. The advantage of the proposed method over existing methods are also verified over with experiments of data‐driven on the IEEE 33 busbar test system. The result of simulation forecasting model indicates that predict closely the busbar outflow power, voltage drop, transformer loading states and power losses to compare with actual load model. [ABSTRACT FROM AUTHOR]

Rampant power theft is a growing concern for power distribution companies as well as the country's economy at the low voltage consumer end. Power theft including theft of power, defective meters, or errors in billing has been an area of growing attention from researchers. Power theft affects the power supply efficiency, raises the demand on the generating station, and causes significant harm to the legitimate customers. This is a major problem in developing countries, and detecting and dealing with the people responsible for theft have been extremely troublesome for utility companies. This paper discusses the factors that influence power theft and pilferages. In this paper, we have given due diligence to the literature review on the methods for detecting power theft. Apparently in a huge democratic setup like India, none of the proposed methods have been very effective so far. There has been no respite from the rampant power thefts, which at times happens in a very organized manner by well-known establishments. There are many power distribution companies active in India. All these companies install energy meters in every house for the measurement of the power consumption. However, the consumers tamper and bypass these devices to connect unauthorized loads sometimes in cohort with the employees of these distribution companies. The change to centralized metering infrastructure for a huge number of consumers is not a feasible solution at all. Thus there is a need for designing alternate low-cost tamper free smart energy meter to detect and communicate the pilferage of power. [ABSTRACT FROM AUTHOR]

—The smart electricity meter (SEM) is a critical element of the smart grid, so power supply company and customers pay many attentions to its service life. SEM is directly employed in the power grid, and there is serious harmonic pollution. The traditional life prediction method of SEM has not considered the influence of the harmonic factor on the reliable life of the SEM, this paper proposes a novel life prediction method of SEM based on the harmonic factor. Based on the data of reliability harmonic test, the harmonic factor is extracted and integrated into the meter life prediction model to improve the accuracy of the prediction. The comparison between the prediction of multiple models of SEMs and the results of accelerated life testing verifies that the method has high accuracy. [ABSTRACT FROM AUTHOR]

Complex energy monitoring and control systems have been widely studied as the related topics include different approaches, advanced sensors, and technologies applied to a strongly varying amount of application fields. This paper is a systematic review of what has been done regarding energy metering system issues about (i) sensors, (ii) the choice of their technology and their characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) the setup of energy Key Performance Indicators (KPIs). The paper provides models about KPI estimation, by highlighting design criteria of complex energy networks. The proposed study is carried out to give useful elements to build models and to simulate in detail energy systems for performance prediction purposes. Some examples of energy complex KPIs based on the integration of the Artificial Intelligence (AI) concept and on basic KPIs or variables are provided in order to define innovative formulation criteria depending on the application field. The proposed examples highlight how modeling a complex KPI as a function of basic variables or KPIs is possible, by means of graph models of architectures. [ABSTRACT FROM AUTHOR]

This paper studies the precise clock management technology for the power consumption information collection system which demands precise requirements of the electric power meter clock. The causes of the clock deviation of the collection system and the problems caused by the clock deviation are analysed first; Then the key technologies for achieving accurate time synchronization are explained; Next, the master station and terminal time synchronization algorithm is designed. Only two NLP synchronizations are required to obtain a high accuracy time deviation, and the accuracy of the algorithm is demonstrated through a test of an example. Finally, the electric power meter time adjustment scheme using a combination of broadcast time adjustment and ESAM point-to-point time adjustment is designed to achieve high accuracy and low error, which has high reliability and practicality. [ABSTRACT FROM AUTHOR]

In this paper, a Comprehensive Diagram Method (CDM) for a Multi-Layer Perceptron Neuron Network (MLPNN) is proposed to realize natural gas energy metering using temperature, pressure, and the speed of sound from an ultrasonic flowmeter. Training and testing of the MLPNN model were performed on the basis of 1003 real data points describing the compression factors (Z-factors) and calorific values of the three main components of natural gas in Sichuan province, China. Moreover, 20 days of real tests were conducted to verify the measurements' accuracy and the adaptability of the new intelligent method. Based on the values of the Mean Relative Errors and the Root Mean Square errors for the learning and test errors calculated on the basis of the actual data, the best-quality MLP 3-5-1 network for the metering of Z-factors and the new CDM methods for the metering of calorific values were experimentally selected. The Bayesian regularized MLPNN (BR-MLPNN) 3-5-1 network showed that the Z-factors of natural gas have a maximum relative error of −0.44%, and the new CDM method revealed calorific values with a maximum relative error of 1.90%. In addition, three local tests revealed that the maximum relative error of the daily cumulative amount of natural gas energy was 2.39%. [ABSTRACT FROM AUTHOR]

With the development of science and technology, electromechanical integration and the Convolutional Neural Network (CNN) have developed rapidly. At present, one of the more widely used fields is the electric energy metering management system. Data analysis is one of the focuses of research in this field. Therefore, this paper introduces CNN algorithm and explains the advantages and disadvantages of the CNN algorithm in previous studies and the direction of optimization. Secondly, the target detection algorithm and data analysis are described, and the application of the target detection algorithm to image information processing and information analysis in the current research is introduced. Additionally, two methods are proposed for optimizing the CNN algorithm, and the optimization model is re-optimized by introducing the migration model. Finally, comparative experiments are conducted to verify the effectiveness and rationality of this model. The experimental results show that the detection rate of the two optimization methods is higher than that of the traditional model. The detection rate of CNN based on Region Proposal Network (RPN) is higher than that based on Region of Interest (ROI) pooling. Simulation experiments are carried out in different power metering management systems in the second experiment. The RPN-CNN model was introduced into the migration model. In system 1, the maximum difference between the detection rate and the traditional model is 0.2. In system 2, the maximum difference in detection rate is 0.12, which verifies the effectiveness of this model. Additionally, the stability of the RPN-CNN is better than that of the traditional model in the slope comparison of the curve, which proves the feasibility of the model. Therefore, this paper has certain reference significance for the data analysis of the power metering management system. [ABSTRACT FROM AUTHOR]

Recent advancements in power systems ensure that the energy being generated is efficiently delivered to meet the growing demand for electricity in many countries. However, determining the proper energy usage by consumers has become a major challenge to utility companies and researchers. Metering is an important aspect of electricity distribution, and it helps to accurately measure and bill customers for their electricity consumption. In this paper, a prototype for an electricity monitoring system using ATMEGA328P as the microcontroller was proposed and developed. The display parameter values are voltage, current, power factor, active and apparent power. A ZMPT101B voltage sensor is used to measure the AC voltage, while an SCT-013 100A current sensor is used to measure the AC current flowing through meter. In addition, a rotary encoder is used to converts the rotational motion into an electrical signal which is used to measure and control various parameters in a system. Real time clock module is based on an internal clock that generates pulses at a fixed frequency. Variety of tests to verify that the system can handle the expected volume of data, can communicate reliably and can secure data were performed, such as logging the energy data to the memory card, testing input sensors, The voltage and current errors for the energy meter are ±0.1% and ±0.3%, respectively while the power value error of ±0.4%, which is acceptable for most metering devices, The use of smart metering system lead to a more sustainable and efficient energy system for achieving a more sustainable future. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) is the practice of estimating power consumption of a single household appliance using data from a total power meter of the user's house. The transformer model has emerged as a popular method for handling NILM problems. However, with the increase in data from electricity meters, there is a need for research focusing on the accuracy and computational complexity of the transformer model. To address this, this paper proposes a sequence-to-sequence load decomposition structure named GTCN, which combines the gate-transformer and convolutional neural networks (CNNs). GTCN introduces a gating mechanism to reduce the number of parameters for training the model while maintaining performance. The introduction of CNNs can effectively capture local features that the gate-transformer may not be able to capture, thereby improving the accuracy of power estimation of individual household appliances. The results of the experiments, based on the UK-DALE dataset, illustrate that GTCN not only demonstrates excellent decomposition performance but also reduces the model parameters compared to conventional transformers. Moreover, the proposed GTCN structure, despite maintaining the same number of model parameters as the traditional transformer architecture after incorporating CNNs, outperforms the conventional transformer model, as well as current seq2seq and R-LSTM technologies, and achieves enhanced prediction accuracy and improved generalization capability. [ABSTRACT FROM AUTHOR]

The Energy Efficiency Directive (EED, Directive 2012/27/EU) has made mandatory the installation of individual metering systems in the case of buildings with centralized heating/cooling and hot water sources (multi-apartment and multi-purpose buildings), provided it is economically and technically feasible. Individual metering of heating/cooling systems is mainly based on thermal energy meters (TEM), which are widely used for direct metering in heating applications. On the other hand, direct metering of energy consumption in cooling systems still represents a challenge, given the different types of cooling units and the lack of regulations from the technical and legal points of view. In this context, this paper briefly overviews the available centralized cooling systems and the possible solutions for metering and sub-metering, which depend on the specific application. Vapour Compression Refrigeration (VCR) systems are spreading worldwide for air conditioning applications. Particular attention has been paid to the direct metering of cooling energy and specifically to refrigerant flow rate measurement, which represents a critical issue because of the small-diameter pipes and the different thermodynamic properties of the fluid used. Thus, an experimental campaign has been developed and carried out in order to compare a clamp-on ultrasonic flowmeter with a more accurate Coriolis one in a direct expansion (DE) system. The experimental tests have been performed at two different temperature conditions, showing a relative error in the mass flow rate measurements within ±10%. [ABSTRACT FROM AUTHOR]

To address the issues of low efficiency, poor security, insufficient compatibility, and difficulties in traceability associated with high-voltage electric energy metering (HVEEM) device verification methods, this paper proposes a design scheme for a remote verification system (RVS) of such devices based on a power cloud platform (PCP). The system adopts the concept of "high-precision local sampling + remote cloud verification" and develops a local acquisition device with compatibility and high precision to achieve fast acquisition of local electrical parameters. The IEC 61850 communication modeling is utilized to establish unified communication standards between the local device and the PCP. The PCP provides two verification methods: physical error verification based on a multi-channel standard and digital verification based on an improved Backpropagation (BP) neural network simulation model. Leveraging the advantages of power cloud technology, the system enables functions such as electrical energy calculation, remote intelligent error verification, cloud storage, condition monitoring, and early warning. Through testing and application, it has been demonstrated that the system achieves an integration accuracy level better than 0.02. It also exhibits good security, compatibility, and traceability of measurement values while attaining a high level of informatization and intelligence. Particularly, the system shows promising prospects for the remote and efficient verification of large-scale and multi-type high-voltage metering devices. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Journal of Civil Engineering / Revista Română de Inginerie Civilă is the property of Matrix Rom and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The power theft is one of the main problems facing the electric energy sector in Iraq, where a large amount of electrical energy is lost due to theft. It is required to design a system capable of detecting and locating energy theft without any human interaction. This paper presents an effective solution with low cost to solve power theft issue in distribution lines. Master meter is designed to measures the power of all meters of the homes connected to it. All the measured values are transmitted to the server via GPRS. The values of power for all energy meters within the grid are also transmitted. The comparison between the power of the master meter and all the other meters are transmitted to the server. If there is a difference between the energy meters, then a theft is happened and the server will send a signal via GSM to the overrun meter to switch off the power supply. Raspberry pi is used as a server and equipped and programmed to detect the power theft. [ABSTRACT FROM AUTHOR]

The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified. [ABSTRACT FROM AUTHOR]

To achieve the carbon peaking and carbon neutrality goals, China promoted county-wide photovoltaic projects (CWPVs) in 2021. A large number of distributed photovoltaic (DPV) installations have expanded the scale of prosumer. It also brings the inequity problem of prosumer, distribution network system operator (DSO) and consumer--DSO and consumer bear the costs such as access cost and depreciation of stock investment caused by DPV access. Combined with China's current electricity tariff system and the promotion of photovoltaic scale development, this paper proposes a standby fee model based on the principle of "beneficiary apportionment". In this model, the standby fee mechanism for self-owned power plants is applied to DPV to reduce the implementation cost of the mechanism, and the principle of "beneficiary apportionment" is adopted to ensure the profitability of DPV. This paper takes the CWPV in Hunan Province, China as an example, calculates the standby fee under various electricity metering methods, and determines the most appropriate standby fee by considering factors such as output uncertainty of DPV. Finally, the difficulties in the implementation of standby fee are sorted out. [ABSTRACT FROM AUTHOR]

We propose to perform an image-based framework for electrical energy meter reading. Our aim is to extract the image region that depicts the digits and then recognize them to record the consumed units. Combining the readings of serial numbers and energy meter units, an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time setup. However, such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display, orientation and scale variations caused by camera positioning, and non-uniform illumination caused by shades. To this end, our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once (YOLO) along with traditional handcrafted features for text extraction and recognition. Our image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image variations. For training and evaluation, the image dataset is annotated to produce the ground truth of all the images. Consequently, YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98% for all the digits. It proves to be robust against the mentioned image variations compared with the traditional handcrafted features. Our proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading, where workers visit door to door, take images ofmeters and manually extract readings from these images. [ABSTRACT FROM AUTHOR]

Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)