Smart Electricity Meters Research Articles

Development of a stochastic virtual smart meter data set for a residential building stock – methodology and sample data

Existing electricity smart meter data sets lack sufficient details on building parameters to evaluate the impact that home characteristics can have on electricity consumption. An extensive, open-source virtual smart meter (VSM) data set with corresponding building characteristics is provided. The methodology used to develop the VSM data is presented in detail. The data set consists of a variety of homes representative of a subset of the Canadian single-family home building stock. The building characteristics cover a wide range of values that are based on probability distributions developed using a segmentation and characterization process. The resulting framework and VSM data set can be used by researchers to develop classification models, verify load disaggregation algorithms, and for a variety of other purposes.

Hedging for Privacy in Smart Water Meters

AbstractSmart 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.

Light-years apart: Energy usage by schools across the South African affluence divide

South Africa’s source of energy in buildings is electricity from a struggling state utility running mainly on coal. But despite the constraints and the high cost, energy usage remains mostly unchecked and poorly managed in schools, whose attention is focused on reducing inequality in educational outcomes. School energy usages have been well studied in many countries but remain largely unexplored in South Africa. We use smart electricity meters to analyse the energy requirements of 16 schools across the country’s polarised affluence spectrum. Comparing their usage with that of schools from other countries, we find that the affluent South African schools have energy densities of 13% as much as those in North America, compared to the poor schools with a mere 5%. We observe a large difference between the daily usage profiles of poor and affluent schools: poor schools used only 40% of the energy used by affluent schools. We ascribe this disparity to constrained infrastructure investment at the poor schools. We identified lighting as a major contributor to energy usage by schools, accounting for as much as 31% of the daily in-term usage by affluent schools and 40% by poor schools, compared to 14 to 25% in developed countries. Since most schools still use fluorescent lights, substantial savings can be achieved by replacing them with efficient LED lights.

Risks, benefits, and control of information: Two studies of smart electric meter privacy

AbstractSmart 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.

Development of a smart electricity meter for households based on existing infrastructure.

Following the universal introduction of information technologies in the electric power industry, it became necessary to create a more perfect measuring infrastructure that would reduce theft of electricity and implement new concepts such as demand response and the P2P market. Existing meters are able to solve the problem of reducing theft, but they do not yet support the functions of P2P trading. This article proposes the development of a smart metering device based on ESP 8266 controller for detection and control of electrical energy and remote turning off or turning on the consumer supply. The meter will be able to exchange information with the cloud platform in real time and provide information about the decline in the quality of electrical energy. It is proposed to use Wi-Fi (main) and GSM (reserve) communication channels.

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM.

Data model for smart electricity meter comprehensive verification based on BP neural network

This paper proposes a BP neural network based model for smart electricity meter (SEM)comprehensive verification under variation conditions of temperature and humidity. Two-neuron BP neural network model and one-neuron BP neural network model are established, respectively. Theoretical results show that when temperature is fixed, the comprehensive verification error is rarely affected by humidity, whereas when humidity is fixed, the SEM comprehensive verification error has obvious variation with temperature which tends to linear correlation.

Research on Verification Error of Smart Electricity Meter based on Regression Analysis

In this paper, the errors of smart electricity meterare verified by using walk-in temperature and humidity box.First of all, this paper investigates the regression analysis method and analyzes the influence of temperature and humidity on the error of the smart electricity meter.The stepwise regression method is selected to model the verification error and the verification conditions of the smart electricity meter and the verification device.Finally, the accuracy of modeling is evaluated.

Marketing and communications channels for diffusion of electricity smart meters in Portugal

National roll-outs of electricity smart meters (ESM) have been undertaken in most of the European countries. The exchange of traditional meters into smart ones is a part of power system transmission into so called smart grids. In these smart grids, the communication and sharing of information happens in real-time and all market players, such as energy suppliers, sellers and consumers, play an important role. As the literature reveals, the successful deployment of ESM requires consumers’ awareness and engagement. That is why, within this paper, we investigate the impact of consumers’ knowledge on what ESM is, as well as the role of marketing platforms: both traditional (i.e. TV or radio) and modern ones (i.e. social media) in ESM diffusion. Based on the on-line survey conducted in Portugal (N = 518), we provide some policy and practical recommendations for energy companies and local authorities regarding the effective usage of marketing platforms and content.

Framework Integrating Lossy Compression and Perturbation for the Case of Smart Meter Privacy

The encoding of high-resolution energy profile datasets from end-users generated by smart electricity meters while maintaining the fidelity of relevant information seems to be one of the backbones of smart electrical markets. In the end-user sphere of smart grids, specific load curves of households can easily be utilized to aggregate detailed information about customer’s daily activities, which would be attractive for cyber attacks. Based on a dataset measured by a smart meter installed in a German household, this paper integrates two complementary approaches to encrypt load profile datasets. On the one hand, the paper explains an integration of a lossy compression and classification technique, which is usable for individual energy consumption profiles of households. On the other hand, a perturbation approach with the Gaussian distribution is used to enhance the safety of a large amount of privacy profiles. By this complete workflow, involving the compression and perturbation, the developed framework sufficiently cut off the chance of de-noising attacks on private data and implement an additional, easy-to-handle layer of data security.

Technology Innovation System Analysis of Electricity Smart Metering in the European Union

Between 2018 and 2023 the penetration rate of electricity smart meters in the European Union (EU) is expected to grow from approximately 44% to 71%. The unprecedently rapid development of smart metering (SM) as an ICT-enabled technological novelty is progressing in a complex, multi-actor innovation system, which is strongly driven by EU-level institutions and policies. This paper presents the comprehensive Technology Innovation System (TIS) analysis of electricity SM development in the EU, with a focus placed on regulatory aspects. The article identifies the key elements of the SM innovation system (technologies and infrastructures; actors and networks; institutions and policies) and characterises their interaction based on an in-depth desk research and a critical assessment of regulations, statistics and primary and grey literature sources (e.g., market reports). The main enablers and barriers for EU-level SM TIS development are studied. The major driving force for EU-level SM TIS is the clear, yet evolving vision of EU-level actors for the SM deployment, founded on the grounds of energy conservation and empowerment of customers. On the other hand, the major inhibitor is the insufficient regulatory framework for roll-outs at the level of a Member State, which does not fully ensure interoperability, data protection and security standards or organisational effectiveness.

Influence of smart metering systems on increasing the accuracy of calculation electrical power losses in electrical networks

The article discusses the possibility of using data from smart electricity meters (SEM) to increase the accuracy of calculation losses in 0.4 kV low-voltage networks. An increase in accuracy can be achieved using actual data about the load graph of electricity consumers (in this case, 6(10)-0.4 kV transformer substations are meant). To date, the operating load factor is taken equal to 0.5, which does not always correspond to the actual data. Using SEM, actual load graphs can be obtained, which enable more accurate determination of the operating load factor. Consequently, the accuracy of calculation of electrical energy losses will be increased.

Nonintrusive Load Monitoring of Variable Speed Drive Cooling Systems

To improve the energy efficiencies of building cooling systems, manufacturers are increasingly utilizing variable speed drive (VSD) motors in system components, e.g. compressors and condensers. While these technologies can provide significant energy savings, these benefits are only realized if these components operate as intended and under proper control. Undetected faults can foil efficiency gains. As such, it’s imperative to monitor cooling system performance to both identify faulty conditions and to properly inform building or multi-building models used for predictive control and energy management. This paper presents nonintrusive load monitoring (NILM) based “mapping” techniques for tracking the performance of a building’s central air conditioning from smart electrical meter or energy monitor data. Using a multivariate linear model, a first mapping disaggregates the air conditioner’s power draw from that of the total building by exploiting the correlations between the building’s line-current harmonics and the power consumption of the air conditioner’s VSD motors. A second mapping then estimates the air conditioner’s heat rejection performance using as inputs the estimated power draw of the first mapping, the building’s zonal temperature, and the outside environmental temperature. The usefulness of these mapping techniques are demonstrated using data collected from a research facility building on the Masdar City Campus of Khalifa University. The mapping techniques combine to provide accurate estimates of the building’s air conditioning performance when operating under normal conditions. These estimates could thus be used as feedback in building energy management controllers and can provide a performance baseline for detection of air conditioner underperformance.

Three-Dimensional Maturity Model of Regional Power Users Against the Background of the Ubiquitous Power Internet of Things

As the Ubiquitous Power Internet of Things (UPIoT) is being developed, the data volume collected by various terminals such as smart electricity meters is increasing daily. The efficient extraction of information from massive data and the provision of value-added services to users, power grids, and market entities are crucial aspects and research directions. A data analysis and application framework for smart meter data based on cloud-fog computing and data contextualization is established, and a data contextualization model based on three-dimensional (3d) maturity analysis of industrial power users is proposed to evaluate the power consumption characteristics of users from the three aspects of the load level, power consumption behavior, and interaction. The results of a case study show that this model allows power grid operators to understand quickly and intuitively the load behavior pattern and power demand of industrial power users, thereby enabling an efficient and accurate assessment of the power supply and an ability to provide value-added services.

Development of Digital Twin for Load Center on the Example of Distribution Network of an Urban District

The paper proposes a concept of building a digital twin based on the reinforcement learning method. This concept allows implementing an accurate digital model of an electrical network with bidirectional automatic data exchange, used for modeling, optimization, and control. The core of such a model is an agent (potential digital twin). The agent, while constantly interacting with a physical object (electrical grid), searches for an optimal strategy for active network management, which involves short-term strategies capable of controlling the power supplied by generators and/ or consumed by the load to avoid overload or voltage problems. Such an agent can verify its training with the initial default policy, which can be considered as a teacher’s advice. The effectiveness of this approach is demonstrated on a test 77-node scheme and a real 17-node network diagram of the Akademgorodok microdistrict (Irkutsk) according to the data from smart electricity meters.

Energy-Efficient Information and Communication System for Smart Meter

The objective of this work is to develop an electricity Smart Meter, allowing the bi-directional energy flow in the Low Voltage (LV) grid. The developed prototype can be deployed in a Photovoltaic (PV) micro-production installation, being able to cooperate with the Distribution System Operator (DSO) regarding remote readings and dynamic voltage control procedures, while keeping the prosumer informed about the active power, as well as odd or faulty situations (e.g., no production). To accomplish the result proposed with functionalities that are useful in the distributed energy grid, this paper offers a device that is standard compliant by using DLMS, providing specific technical information to the DSO. Additionally, it has two low power wireless communication technologies: Bluetooth Low Energy (BLE), for short range local readings and configuration by the prosumer or maintenance teams; and Sigfox, a long range low power technology, which has a permanent storage and facilitates consultation of past and current records. The latter can be accessed by an Android application from a remote location.

Capacitated Multicommodity Flow Problem for Heterogeneous Smart Electricity Metering Communications Using Column Generation

This paper addresses the planning and deployment of wireless heterogeneous networks (WHNs) for smart metering, based on a cross-layer solution. We combine the constraints of the network layer that considers routing and flow demands at each link in the WHN, while at the same time, we account for the restrictions of the physical layer referred to the capacity of a short range technology when used in a multi-hop fashion. We propose a model based on a column generation approach to solve the capacitated multicommodity flow problem (CMCF); the model includes wireless links capacities, coverage, and cost. The work integrates the multi-hop routing of packets in a mesh network formed by smart meters and concentrators connected to a cellular network via base stations. The traffic of each link is represented in a multigraph with the occupation percentage, and we build a scalable routing tree on a georeferenced map to represent a real deployment. The results describe the behavior of the proposed model in terms of the traffic load per concentrator, the network coverage, and the reduction of energy consumption. We demonstrate that an infrastructure cost reduction is achieved with the inclusion of multi-hop short range technology, which reduces the number of smart meters that require a direct connection to cellular technology. The model guarantees 100% coverage of the smart meters analyzed in each scenario. The calculation time of the CMCF for advanced metering infrastructure (CMCF-AMI) based on the column generation algorithm as the population increases is reduced by 10%, and this is the expected return when the population is considerable.

Electricity theft detection by sources of threats for smart city planning

Smart city adoption and deployment has taken the centre stage worldwide with its realisation clearly hinged on energy efficiency, but its planning is threatened by the vulnerability of smart grids (SGs). Adversaries launch attacks with various motives, but the rampaging electricity theft menace is causing major concerns to SGs deployments and consequently, energy efficiency. Smart electricity meters deployments via the advanced metering infrastructure present promising solutions and even greater potential as it provides adequate data for analytical inferences for achieving proactive measures against various cyberattacks. This study suggests the sources of threats as the first step of such proactive measures of curbing electricity thefts. It provides a framework for monitoring, identifying and curbing the threats based on factors indicative of electricity thefts in a smart utility network. The proposed framework basically focuses on these symptoms of the identified threats indicative of possible electricity theft occurrence to decide on preventing thefts. This study gives a useful background to smart city planners in realising a more reliable, robust and secured energy management scheme required for a sustainable city.

Design and application of the automatic AC voltage test device for smart electricity meters and data acquire terminal of the special transformer

The AC voltage test is an important means to effectively detect the insulation defects of the equipment. The problems of low efficiency, poor safety, and poor compatibility of the AC voltage test for smart electricity meters and data acquire terminal of the special transformer, the fully automatic AC voltage test device are studied and designed by software programming, PLC control technology, and automatic crimping technology. The automatic test and result judgment of the AC voltage test for the smart electricity meters and data acquire terminal of the special transformer are realised, which makes many problems of the existing test equipment to be effectively solved. Thus, the efficiency, compatibility, safety and reliability of the test are improved.

Hybrid Deep Neural Networks for Detection of Non-Technical Losses in Electricity Smart Meters

Non-technical losses (NTL) in electricity utilities are responsible for major revenue losses. In this paper, we propose a novel end-to-end solution to self-learn the features for detecting anomalies and frauds in smart meters using a hybrid deep neural network. The network is fed with simple raw data, removing the need of handcrafted feature engineering. The proposed architecture consists of a long short-term memory network and a multi-layer perceptrons network. The first network analyses the raw daily energy consumption history whilst the second one integrates non-sequential data such as its contracted power or geographical information. The results show that the hybrid neural network significantly outperforms state-of-the-art classifiers as well as previous deep learning models used in NTL detection. The model has been trained and tested with real smart meter data of Endesa, the largest electricity utility in Spain.