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The growing adoption of smart meters enables the measurement of households' energy consumption, influenced not solely by building characteristics such as thermal insulation but also by residents' behavioural patterns, such as heating and ventilation practices. To motivate residents to adopt more sustainable behaviours, user interfaces on smartphones and laptops are increasingly using consumption data from households' smart meters to enable effective goal‐setting. In contrast to previous research largely focusing on goal‐setting in isolation, this study examines the role of specific social comparison‐related design features that future research and practitioners can consider along with goal‐setting to stimulate sustainable behaviours. Specifically, we look into the influence of residents' perception of their relative performance (i.e., whether their behaviour was better or worse than a reference group) on their ambition to act (i.e., targeted improvement goal) and their actual energy consumption behaviour. Moreover, we investigate the influence of a goal's evaluative standard (i.e., whether the goal refers to one's own or other's performance) on the relationship between relative performance, ambition to act, and energy consumption behaviour. Drawing on social comparison theory, we conducted a framed field experiment with 152 households. We find that a goal's evaluative standard influences residents' awareness of their relative performance, affecting their ambition to act and, ultimately, their energy consumption behaviour. More specifically, we find that whereas other‐ (vs. self‐) referencing goals encourage residents from worse‐than‐average performing households more strongly to improve their energy consumption behaviour, they discourage better‐than‐average ones. Overall, our study provides novel insights into the interplay between relative performance and evaluative standards as a means of fostering social comparison in smart meter‐facilitated goal‐setting, highlighting their crucial role in effectively supporting sustainable behaviours. [ABSTRACT FROM AUTHOR]

Smart meters monitor hourly water consumption patterns while reducing the cost and inconvenience of traditional meters. This study comprehensively analyzes 1871 households that previously used traditional meters from the distribution point to the distribution area. All the households were equipped with smart meters and the data collected were used for analysis. On the basis of the total estimated water consumption, 227 households were classified as single households. These households were further classified into single-worker and -nonworker households. This study analyzed smart meter data to evaluate the timings and amounts of water consumption peaks. The results indicate that worker households peaked at 8:00, with 29 L/h of consumption on weekdays, and peaked again on evenings at 20:00–21:00, averaging 32 L/h. For nonworker households, the peak occurred at 9:00, with 20 L/h, with no major changes in the afternoon, and a second peak occurred at 19:00–20:00 in evening, with an average of 19 L/h. Moreover, worker households peaked at 8:00 and 20:00 on weekdays, and at 9:00 and 19:00 on weekends. It was revealed that worker households consume 10% more on weekends than on weekdays, and 262% more from 13:00 to 16:00. These findings may assist in water supply planning by supporting distribution schedules on the basis of peak household consumption, leading to more helpful resource management. [ABSTRACT FROM AUTHOR]

A smart meter is an automation technology that sends real-time power consumption of electric appliances to the outsourced cloud through the aggregator node. An outsourced cloud is used by the Utility providers to release computation and storage overhead. The real-time smart meter data helps in the management of demand and supply in the smart grid. However, the real-time smart meter data exposes the privacy of smart meter customers and inefficient aggregated smart meter data results in unbalanced power management decisions in the smart grid. Therefore, a smart meter data storage (SMDS) model is proposed that aggregates the encrypted smart meter data at the fog node with the property of homomorphic encryption and stores it on the outsourced cloud. Two clouds are used to process the smart meter data and only the utility provider is able to retrieve the actual power consumption of the smart meter. Additionally, a secure query processing model is designed to retrieve the smart meter data on the outsourced cloud. Experimental results show the effectiveness of the proposed work and the feature comparison demonstrates the superiority of the proposed over the existing works. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R2 value. [ABSTRACT FROM AUTHOR]

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device's continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%. [ABSTRACT FROM AUTHOR]

The Smart Grid represents a modernized electrical infrastructure integrating information and communication technology for bidirectional data exchange between power providers and consumers. This advancement enables seamless digital connectivity among intelligent devices such as Smart Meters, Demand Response Control Units, and Service Providers, facilitating remote data management for optimized energy distribution. However, the reliance on unsecured wireless communication channels poses significant security vulnerabilities, including replay, impersonation, man-in-the-middle, and physical capture attacks. To address these challenges, this study introduces a pioneering approach called Deep-Learning and Blockchain-enabled Secure Data Sharing. Specifically, Deep-Learning techniques are leveraged to develop an effective Intrusion Detection System. The proposed RENS (intRusion detEction aNd clasSification) combines Variational AutoEncoder with Attention-based Bidirectional Long Short-Term Memory for feature extraction and attack detection. Moreover, normal instances identified by RENS are utilized in a blockchain-based access control mechanism, ensuring secure and immutable data exchange among Smart Grid entities. In this framework, participating Service Providers form a peer-to-peer network responsible for generating blocks associated with individual SMs. These blocks undergo validation and are appended to a private blockchain ledger using a smart contract-based Proof-of-Authentication consensus mechanism. Experimental results and security analysis demonstrate the superiority of the DBSDS framework over conventional BiLSTM techniques, confirming its effectiveness in safeguarding Smart Grid operations. [ABSTRACT FROM AUTHOR]

The transactive energy market (TEM) is a recent development in energy management that enables prosumers to trade directly, promising many environmental and economic benefits. Prosumer trading necessitates sharing information to facilitate transactions. Additionally, many TEMs propose using blockchains to manage auctions and store transactions. These facts introduce privacy concerns: consumption data, trading history, and other identifying information pose risks to users if leaked. Anonymity by trading under a pseudonym is commonly presented as a solution; however, this creates risks for market participants: scammed users will not have recourse, and users with innocent malfunctions may be banned from trading. We propose the Individually Linkable Pseudonymous Trading Scheme (ILPTS), which enables users to trade under a pseudonym, protecting their identity, while a smart contract monitors reputations and can temporarily deanonymize a user, ensuring market integrity. ILPTS was developed in stages. Examination of existing TEM literature was performed to identify desirable features. Analysis of cryptography literature was performed to identify techniques that may confer certain features. It was found through formal analysis that ILPTS adheres to identified design goals, improves upon existing solutions, and resists common attacks against TEMs. Future work includes software simulation and on-device implementation to further verify security and feasibility. [ABSTRACT FROM AUTHOR]

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature. [ABSTRACT FROM AUTHOR]

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized. [ABSTRACT FROM AUTHOR]

The exploitation of green hydrogen blending with natural gas (NG) is expected to contribute in lowering greenhouse gas emissions and fostering the share of renewable energy sources (RES). The NG infrastructure, in fact, can act as a storage facility for green hydrogen produced from excess RES, also providing flexibility to the electric system. However, the unbalance of the networks and the raise of protection issues for consumers may occur as a consequence of the potential decay of the metrological performance of gas meters operating with natural gas and hydrogen blends. In this paper, the authors present the results of an experimental campaign aimed at analysing the effect of hydrogen blending on the accuracy of smart domestic gas meters. Experimental tests were performed in the laboratory using air, natural gas and gas-hydrogen blends up to 23%vol. The results show no criticalities occur for diaphragm and ultrasonic gas meters, regardless of the hydrogen content. On the other hand, thermal mass gas meters show good reliability when hydrogen blends are within 2%vol, whereas for higher hydrogen contents, reliability is demonstrated only when they are equipped with a specific new design routine capable to address the gas quality changes. • Hydrogen blending with natural gas is becoming a spread practice for green energy storage and use. • Issues related to the readiness of the gas infrastructure to distribute hydrogen blends and appliances still have to be investigated. • Consumer protection and fairness of the billing is a crucial issue for domestic gas meters. • Accuracy tests of domestic gas meters of different measuring principles with hydrogen admixture up to 23% are presented. [ABSTRACT FROM AUTHOR]

Due to recent technical breakthroughs in wireless communication and the Internet of Things (IoT), the smart grid (SG) has been recognized as a next-generation network for intelligent and efficient electric power transmission. Electric vehicle charging is becoming one of the most popular SG application. However, in SG environment the communication between a vehicle user and smart meter is mostly performed using insecure channel for managing demand response during peak hours. This raises serious security and privacy issues. Motivated from the aforementioned challenges, this paper presents a secure and privacy-preserving framework for IoT-enabled SG environment. The proposed framework first uses a secure mutual authentication scheme to register and exchange session key among SG participants. Second, a deep learning method that uses a stacked sparse denoising autoencoder to convert data into a new encoded format is suggested. The attention-based truncated long short-term memory uses this modified data to identify intrusions. The proposed blockchain architecture uses the proof of authentication consensus mechanism to propagate regular transactions in order to validate data integrity and prevent data poisoning attacks. The suggested framework outperforms several current state-of-the-art solutions in terms of security and numerical discoveries. [ABSTRACT FROM AUTHOR]

The Internet of Things is an ecosystem that connects billions of smart devices, meters, and sensors. These devices and sensors collect and share data for use and evaluation by organizations in different industry sectors. Humans may use the IoT to live and work more intelligently and gain total control over their lives. Consequently, IoT can be used to connect devices and integrate them with new digital technologies for customers. On the other hand, smart utility companies in the electric, gas, and water sectors need to deliver services more efficiently and analyze their operations in a way that can help optimize performance, detect growing problems in real time, and initiate fixes to avoid unplanned service interruptions. Building actual smart metering networks is costly and time-consuming. Therefore, in this paper, a new Smart Utilities Traffic Scheduling Algorithm (SUTSA) is proposed. To minimize the system complexity, the model is based on narrowband power line communication, in which a wired hidden network sends data across power lines. A simulation is performed using OPNET Modeler 14.5 to evaluate the proposed model. The results proved that the proposed model is highly scalable and achieves full network-bandwidth utilization in different situations based on different application requirements. [ABSTRACT FROM AUTHOR]

Home area network (HAN) devices send the electricity consumption and other important data to the smart meter that must remain confidential from other devices. This paper proposes a novel one-dimensional hybrid chaotic map. The proposed map shows excellent chaotic properties when analyzed by bifurcation diagram, Lyapunov exponent & Shannon entropy. We further design an encryption strategy for data transfers between the smart meter and HAN devices. The proposed encryption scheme uses the existing lightweight key management in advanced metering infrastructure (LKM-AMI) architecture for data transfers, in which the encrypted data is transferred through an insecure channel and private keys are provided by trusted third party (TTP) through secure channels. The 2-way communication between HAN devices and the smart meter sends messages that are encrypted by using the proposed novel hybrid one-dimensional chaotic map. The encryption strategy mainly consists of three steps. In the first step, the seed and the control parameters are initialized. The second phase generates two intermediate keys using the proposed hybrid chaotic map. In the last phase, we encrypt the message by applying permutation followed by diffusion using intermediate keys. The proposed encryption strategy is resistant to various attacks. [ABSTRACT FROM AUTHOR]

The incorporation of renewable energy sources necessitates the upgrade of the electrical grid to a smart grid, which involves the implementation of smart meters. Although smart meters provide benefits to users, many smart meter implementation projects have failed to be accepted by users. This article assesses the factors influencing the acceptance of household smart meters in Joinville, a city in the south of Brazil. Based on the Unified and Extended Theory of Acceptance and Use of Technology (UTAUT2), a structural equation model was estimated using data from a sample of 136 respondents in the city of Joinville. The results indicate that Performance Expectancy, Hedonic Motivation, and Social Influence constructs have a more substantial effect on the Intention to Use smart meters. The results provide evidence for planning the upgrade of the electrical grid by implementing smart meters in southern Brazil. [ABSTRACT FROM AUTHOR]

This paper proposes three methods of the optimal smart meter selection for acting as a data concentrator in the automatic meter reading last mile network. The study explains the reasons why the selected smart meter should also act as a data concentrator, in addition to its basic role. To select the smart meter, either the reliability of communication or the speed of the automatic meter reading process was considered. Graph theory is employed to analyse the last mile network, described as sets of nodes and unreliable links. The frame error ratio was used to assess the unreliability whilst the number of hops was used to describe the speed of the reading process. The input data for the analysis are qualitative parameters determined based on observations in the real, operated last mile networks as well as their typical topological arrangements. The results of the research can be useful in the last mile network migration process, which uses concentrators to the networks without them, or during the process of newer last mile network implementation, where data concentrators are no longer applicable. The efficiency of the proposed methods is assessed measurably. [ABSTRACT FROM AUTHOR]

A smart grid is an electricity network that uses advanced technologies to facilitate the exchange of information and electricity between utility companies and customers. Although most of the technologies involved in such grids have reached maturity, smart meters—as connected devices—introduce new security challenges. To overcome this significant obstacle to grid modernization, safeguarding privacy has emerged as a paramount concern. In this paper, we begin by evaluating the security levels of recently proposed authentication methods for smart meters. Subsequently, we introduce an enhanced protocol named PPSG, designed for smart grids, which incorporates physical unclonable functions (PUF) and an elliptic curve cryptography (ECC) module to address the vulnerabilities identified in previous approaches. Our security analysis, utilizing a real-or-random (RoR) model, demonstrates that PPSG effectively mitigates the weaknesses found in prior methods. To assess the practicality of PPSG, we conduct simulations using an Arduino UNO board, measuring computation, communication, and energy costs. Our results, including a processing time of 153 ms, a communication cost of 1376 bits, and an energy consumption of 13.468 mJ, align with the requirements of resource-constrained devices within smart grids. [ABSTRACT FROM AUTHOR]

Water utilities in Japan face a number of challenges, including declining water demand due to a shrinking population, shrinking workforce, and aging water supply facilities. Widespread use of smart water meters is crucial for solving these problems. The widespread use of smart water meters is expected to bring many benefits such as reduced labor by automating meter reading, early identification of leaks, and visualization of pipeline data to strengthen the infrastructure of water services, business continuity, and customer service, as detailed data can be obtained using wireless communication. Demonstration tests are actively conducted in Japan; however, many problems have been reported with cast iron meter boxes blocking radio waves. To address the issue, a low-cost slit structure for cast iron meter boxes is investigated in this study. The results confirm that the L-shaped tapered slit array structure with a cavity, which can be fabricated in a cast iron integral structure, satisfies the design loads required for road installation. The proposed slit structure achieved gain characteristics from −3.32 to more than 9.54 dBi in the 800 to 920 MHz band. The gain characteristics of conventional cast iron meter boxes range from −15 to −20 dBi, and the gain has been significantly improved. Antennas with a gain of −2.0 to +1.5 dB (0.8 to 2.5 GHz) were used for the transmitter antenna, which was found to have a higher gain than the transmit antenna in the 800 to 880 MHz frequency band. In the 1.5 to 2.0 GHz band, a high peak gain of 4.25 dBi was achieved at 1660 MHz, with no null and the lowest gain confirmed that this is an improvement of more than 10 dBi over conventional products. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption. [ABSTRACT FROM AUTHOR]

This article summarizes and organizes recent research findings in information and communication technology security developments integrated with smart grids. A vital component of a smart grid is a smart meter. It is relevant because it can collect, process, and transport customer's data over the Internet. Whereas developments in smart grid and smart meter technologies have given new productivity gains, they have also presented new security concerns. Security is essential in defending both the smart grid and smart meter from cyber-attack. Guaranteeing safety is one of the most challenging aspects of designing and deploying a smart metering infrastructure. This study presents a thorough investigation of the integrity of smart metering technologies from multiple different viewpoints. It focuses on threats, countermeasures, and estimations. This article makes four contributions: First, all potential vulnerabilities in smart metering components are described and examined. Second, it assesses the impact of attacks that use these weaknesses to boost the performance of each part and the whole smart meter structure. Thirdly, potential countermeasures to defend smart meters are discussed. lastly, it discusses the unresolved issues surrounding smart meter security and future research areas. This evaluation is distinctive due to its exhaustive treatment of security weaknesses and attacks on smart meter components. In conclusion, the future vision is described. [ABSTRACT FROM AUTHOR]

State estimation for distribution systems faces the challenge of dealing with limited real-time measurements and historical data. This work describes a Bayesian state estimation approach tailored for practical implementation in different data availability scenarios, especially when both real-time and historical data are scarce. The approach leverages statistical correlations of the state variables from a twofold origin: (1) from the physical coupling through the grid and (2) from similar behavioral patterns of customers. We show how these correlations can be parameterized, especially when no historical time series data are available, and that accounting for these correlations yields substantial accuracy gains for state estimation and for the recognition of critical system states, i.e., states with voltage or current limit violations. In a case study, the approach is tested in a realistic European-type, medium-voltage grid. The method accurately recognizes critical system states with an aggregated true positive rate of 98%. Compared to widely used approaches that do not consider these correlations, the number of undetected true critical cases can be reduced by a factor of up to 9. Particularly in the case where no historical smart meter time series data is available, the recognition accuracy of critical system states is nearly as high as with full smart meter coverage. [ABSTRACT FROM AUTHOR]

Smart microgrids are being increasingly deployed within the Department of Defense. The microgrid at Marine Corps Air Station (MCAS) Miramar is one such deployment that has fostered the integration of different technologies, including 5G and Advanced Metering Infrastructure (AMI). The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and identify specific cyber-attacks against this infrastructure. Both network traffic in the form of packet captures (PCAP) and time series data (from the AMI sensors) are considered. We train the autoencoder model on three traffic data sets: (1) Modbus TCP/IP PCAP data from the hardwired network apparatus of the smart microgrid, (2) experimentally generated 5G PCAP data that mimic traffic on the smart microgrid and (3) AMI smart meter sensor data provided by the Naval Facilities (NAVFAC) Engineering Systems Command. Distributed denial-of-service (DDoS) and false data injection attacks (FDIA) are synthetically generated. We show the effectiveness of the autoencoder on detecting and classifying these types of attacks in terms of accuracy, precision, recall, and F-scores. [ABSTRACT FROM AUTHOR]

Power utilities worldwide are facing enormous challenges when it comes to the distribution of electricity. With these challenges, electricity theft is regarded as the most common challenge in the electrical distribution system. Electricity theft can be meter tampering done in consumer houses and illegal connections done using hook-ups from the distribution pole grids. These electricity theft challenges have caused power utilities to reconsider customer engagements focusing on feedback, putting loss detection systems in their distribution system networks, using artificial intelligence to schedule maintenance and other asset management activities, etc. The main focus of this paper is to design a real-time power theft monitoring and detection system that is able to detect power theft in distribution systems. This proposed system utilizes smart meters consisting of an Arduino ATMega328P microcontrollers with GSM modules (Global System for Mobile Communication) used for system communication. Cloud storage is created to store the smart meter data. Simulations of the proposed system were done using Proteus Design Suite v.8.10 SP3 software. The proposed system is practically constructed for prototype measurement results. Should power imbalances be measured by the system, the authority office will receive an SMS notification as an alert for power theft detected by a specific smart metering system. The authority office will analyse the power measurements sent to the cloud storage (MATLAB Online, ThingSpeak IoT channels display), and further action will be taken. [ABSTRACT FROM AUTHOR]

Issues such as climate change, water scarcity, population growth, and distribution losses have stimulated the use of new technologies to manage water resources. This is how the concept of smart water management emerged as a subcategory of the concept of smart cities. This article aimed first to identify the applications of smart water-management systems described in academic articles either as applications in development or as applications already implemented or as future trends; and, second, to classify them according to the processes in the value chain of public water supply services. To this end, a systematic review of the literature was carried out, in which 100 mentions of applications were identified in 62 selected articles; then, the mentions were grouped into 10 categories. The most frequent application categories were smart meters, implementation models and architectures, and loss management. Among the processes of the value chain, applications in processes of distribution and water use were highly predominant. The lack of detail about the integration between the different applications for a smart water-management system was pointed out as a limitation and an opportunity for future research development, especially in terms of a technological roadmap study based on the relationship between smart meters and loss management. [ABSTRACT FROM AUTHOR]

The demand response program is an important feature of the smart grid. It attempts to reduce peak demand, improve the smart grid efficiency, and ensure system reliability. Implementing demand-response programs in residential and commercial buildings requires the use of smart meters and smart plugs. In this paper, we propose an architecture for a home-energy-management system based on the fog-computing paradigm, an Internet-of-Things-enabled smart plug, and a smart meter. The smart plug measures in real-time the root mean square (RMS) value of the current, frequency, power factor, active power, and reactive power. These readings are subsequently transmitted to the smart meter through the Zigbee network. Tiny machine learning algorithms are used at the smart meter to identify appliances automatically. The smart meter and smart plug were prototyped by using Raspberry Pi and Arduino, respectively. The smart plug's accuracy was quantified by comparing it to laboratory measurements. To assess the speed and precision of the small machine learning algorithm, a publicly accessible dataset was utilized. The obtained results indicate that the accuracy of both the smart meter and the smart plug exceeds 97% and 99%, respectively. The execution of the trained decision tree and support vector machine algorithms was verified on the Raspberry Pi 3 Model B Rev 1.2, operating at a clock speed of 600 MHz. The measured latency for the decision tree classifier's inference was 1.59 microseconds. In a practical situation, the time-of-use-based demand-response program can reduce the power cost by about 30%. [ABSTRACT FROM AUTHOR]

The decision tree algorithm-based load identification (DTA-LI) system’s fusion data method is crucial in the monitoring of appliance loads for the purposes of improving energy efficiency and management. Common home electrical devices are identified and classified from smart meter data through the analysis of voltage and current variations, allowing for the measurement of energy usage in residential buildings. A load identification system based on a decision tree algorithm may infer information about the residents of a building based on their energy usage habits. Better power savings rates, load shedding management, and overall electrical system performance are the results of the clusters’ ability to capture families’ purchasing patterns and geodemographic segmentation. The DTA-LI system’s fusion data method presents a promising avenue for improving residential buildings’ energy performance and lowering their carbon footprint, especially in light of the widespread use of smart meters in recent years. [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]

More energy-consuming devices such as household electronics and more production facilities worldwide are causing increases in electricity demand and energy prices. The public service has difficulty maintaining the energy balance. These problems can be overcome by rapidly transforming the traditional electricity grid into a smart grid (SG) infrastructure. Smart meters (SMs) are an essential component of SGs and have vital tasks. This study has developed an Internet of Things (IoT) based SM that can reach high data rate of 38,400 bps or frequency of 160 MHz, using SQL Server for data storage and bidirectional data transmission with 174 W total load. In this way, consumers can track their energy consumption hourly, daily, and monthly, learn how much they spent on consumption, and receive warnings for a power outage. A fuzzy system and mobile application software are integrated into the SM structure for all these purposes. The designed device is believed to contribute significantly to the spread of SGs due to all these features. [ABSTRACT FROM AUTHOR]

The performance and stability of the electric network are improved through smart grid technologies. The smart grid strongly rely on digital communication technologies creates security issues that must be addressed to distribute power efficiently and safely. For billing, load balancing, and energy management, the customer‐side smart meter regularly transmits the consumption reading to the system operator. However, dishonest customers created cyberattacks by entering fictitious readings of their electrical consumption in order to steal electricity for financial benefit. The distributed monitoring meter‐based network concept is utilized in this paper to closely monitor the losses. A two‐stage clustering‐based technique has also been suggested for the purpose of detecting theft. The approach can be detect the customer ID having abnormality in consumption pattern. To verify the effectiveness of the suggested strategy, numerical experiments on two datasets of smart meters have been conducted and four different types of attack patterns have been constructed. Simulation results show that the proposed scheme is very effective for detecting various types of attack patterns. In addition, the paper helps to predict how much power and financial loss the company will suffer as a result of multiple attacks. [ABSTRACT FROM AUTHOR]

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications. [ABSTRACT FROM AUTHOR]

Advanced metering infrastructure (AMI) is one of the core aspects of the smart grid, and offers numerous possible benefits, such as load control and demand response. AMI enables two-way communication, but it is vulnerable to electricity theft. Due to the tempering of the smart meter, the abnormal pattern of fraud becomes difficult to detect, which introduces the increment of false data consumption. The majority of existing methods depend on factors such as predefined limits, extra information requirements, and the desire for labelled datasets. These factors are difficult to realize or have a poor degree of identification. This paper integrates two novel techniques to detect the False Data Injection attack. One is Principal Component Analysis, which is based on feature correlation, second is an unsupervised learning based technique Density-Based Spatial Clustering of Applications with Noise which helps to identify data patterns to detect outliers from a huge number of load profiles. The combination makes the proposed technique an appropriate tool for detecting an arbitrary pattern attack in high-dimensional data. Four different attack scenarios are analyzed on the Irish Science Data Archive smart meter data set. The efficacy of proposed theft detection method is evaluated by comparing the AUC, mAP, and time with those of other clustering approaches. The detection rate of the proposed scheme has been compared with the other approaches in the literature. The results demonstrate that the detection rate is substantially higher than that of other theft detection methods. Finally, the influence of varied abnormality ratios has been investigated. [ABSTRACT FROM AUTHOR]

Smart meters are rapidly installing by utility providers to improve the reliability and performance of Smart Grid. Utility providers analyze real-time smart meter data to monitor, predict, generate and distribute power. The customer's real-time activity and power usage can be revealed by analyzing the smart meter data. Therefore, the security and privacy of the data is a crucial issue for the smart grid. This paper proposes a secure and privacy-preserving data aggregation and classification (SP-DAC) model based on fog and cloud architecture. Data is aggregated at the fog node in the SP-DAC model, and classification is performed at the outsourced cloud with three machine learning classifiers. Simulation results analyze the cryptographic costs and classification performance. Real-world smart meter dataset "UMass Smart" is taken for experiments and classification accuracy, precision, recall, and F1 score achieved upto 88%, 87%, 90%, and 88%, respectively. The comparison with existing models shows the superiority of the SP-DAC model in terms of features and parameters. [ABSTRACT FROM AUTHOR]

For ten years Polish distribution companies have implemented several AMR and AMI pilot projects. In Poland, the Distribution System Operator (DSO) equips households with smart energy meters (e.g. in the United Kingdom - the energy supplier, which can be changed by the customer). Over these years the operators collected a lot of experience with these pilot projects. The author of this publication describes his personal experience in the research projects in which he was involved, directly or indirectly, as well ones of which he has received information about from the DSOs. This article does not present the experiences from all major AMI system projects in Poland, but only from few projects in which the author of this article participated. The author participated in projects in which the smart meters utilize a variety of different technologies: PLC, GPRS, HSCSD, RS-232, etc. In this article, however, he focuses only on his experience in working with PLC technology. This paper presents the requirements, considerations and experience gained in the development and pilot implementation of AMI systems in Poland. [ABSTRACT FROM AUTHOR]

Electric smart grids enable a bidirectional flow of electricity and information among power system assets. For proper monitoring and controlling of power quality, reliability, scalability and flexibility, there is a need for an environmentally friendly system that is transparent, sustainable, costsaving, energy-efficient, agile and secure. This paper provides an overview of the emerging technologies behind smart grids and the internet of things. The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply. In addition, the paper sorts out challenges in the traditional or legacy electricity grid, power generation, transmission, distribution, and revenue management challenges such as reducing aggregate technical and commercial loss by reforming the existing manual or semi-automatic techniques to fully smart or automatic systems. This article represents a concise review of research works in creating components of the smart grid like smart metering infrastructure for postpaid as well as in prepaid mode, internal structure comparison of advanced metering methods in present scenarios, and communication systems. [ABSTRACT FROM AUTHOR]

Human activity event recognition (HAER) within a residence is a topic of significant interest in the field of ambient assisted living (AAL). Commonly, various sensors are installed within a residence to enable the monitoring of people. This work presents a new approach for HAER within a residence by (re-)using measurements from commercial smart water meters. Our approach is based on the assumption that changes in water flow within a residence, specifically the transition from no flow to flow above a certain threshold, indicate human activity. Using a separate, labeled evaluation data set from three households that was collected under controlled/laboratory-like conditions, we assess the performance of our HAER method. Our results showed that the approach has a high precision (0.86) and recall (1.00). Within this work, we further recorded a new open data set of water consumption data in 17 German households with a median sample rate of 0.08 3 ¯ Hz to demonstrate that water flow data are sufficient to detect activity events within a regular daily routine. Overall, this article demonstrates that smart water meter data can be effectively used for HAER within a residence. [ABSTRACT FROM AUTHOR]

The privacy of electricity consumers has become one of the most critical subjects in designing smart meters and their proliferation. In this work, a multilayer architecture has been proposed for anonymous data collection from smart meters, which provides: (1) The anonymity of information for third‐party data consumers; (2) Secure communication to utility provider network for billing purposes; (3) Online control of data sharing for end‐users; (4) Low communication costs based on available Internet of things (IoT) communication protocols. The core elements of this architecture are, first, the digital twin equivalent of the cyber‐physical system and, second, the Tangle distributed ledger network with IOTA cryptocurrency. In this architecture, digital twin models are updated in real‐time by information received from trusted nodes of the Tangle distributed network anonymously. A small‐scale laboratory prototype based on this architecture has been developed using the dSPACE SCALEXIO real‐time simulator and open‐source software tools to prove the feasibility of the proposed solution. The numerical results confirm that after a few seconds of anomaly detection, the microgrid was fully stabilized around its operating point with less than 5% deviation during the transition time. [ABSTRACT FROM AUTHOR]

As a rapidly evolving paradigm, digital transformation (DT) remains one of today's most significant challenges. Consequently, numerous businesses devote a substantial portion of their resources to this captivating transition. However, this investment's return is difficult to quantify or even unexplored by others. This observation constituted the foundation for Solow's productivity paradox. Even if they are aware of how the KPI measurement affects their performance, managers continue to disregard this evaluation. In terms of academic literature, this field of study is still inadequately developed. To address this limitation, this study uses the NVivo software to conduct a systematic literature review highlighting the KPIs, the methodology used in the corpus, the evolution of related works, and their type. According to our analysis, several works cite this term without describing it or defining a general approach to DT metrics. In addition, the financial and commercial performance KPIs are the most specific. Following a summary of the primary KPIs in the literature, we classified them into two prominent families: generic and specific. This classification represents the originality of this paper. The generic KPIs are multidimensional and applicable to a variety of businesses, while the specific ones pertain to a particular function or industry. [ABSTRACT FROM AUTHOR]

Smart meters measure, control, analyze, and predict the amount of electricity, water, and gas used. In developing countries, where there is no consensus to accept the use of smart meters, there are many possible risks when using smart meters. However, there are also many benefits of smart meters. This study conducts an overall assessment of the demand and impact of the smart meter in the southern region of Vietnam through a survey of 500 samples. This article examines information technology system (IS) related factors and engineering model-related factors according to technical readiness such as optimism, innovation, insecurity, and discomfort. Accompanying that is the expectation of smart meters, for Vietnamese people's intention to constantly use smart meters. Most of the previous studies on smart meter systems have focused on analyzing the impact of factors affecting the application using single-step structural equation modeling (SEM). In this study, it is proposed to use a 2-layer model between the research model of the multi-analysis method by combining the Partial Least Squares - Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) model was performed for additional analysis for the results of PLS-SEM, and ANN has higher predictive accuracy than PLS-SEM because ANN has the ability to perform well for both linear relational model and linear relationship model and non-linear with high prediction. First, the PLS-SEM model evaluates the factors affecting the intention to use the smart meter system. Second, the ANN ranks the impact factors of the critical predictors from the PLS-SEM model, and the Critical Performance Map Analysis (IPMA) analyzes the exact results for the critical performance of the variables elements. The results of this study show that the quality of information and quality of system factors of the IS model have a negative impact on users' intention to use smart meters. At the same time, factors of Optimization and innovation have a strong positive impact on users' intention to use smart meters. [ABSTRACT FROM AUTHOR]

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The implementation of the smart grid (SG) and cyber-physical systems (CPS) greatly enhances the safety, reliability, and efficiency of energy production and distribution. Smart grids rely on smart meters (SMs) in converting the power grids (PGs) in a smart and reliable way. However, the proper operation of these systems needs to protect them against attack attempts and unauthorized entities. In this regard, key-management and authentication mechanisms can play a significant role. In this paper, we shed light on the importance of these mechanisms, clarifying the main efforts presented in the context of the literature. First, we address the main intelligent attacks affecting the SGs. Secondly, the main terms of cryptography are addressed. Thirdly, we summarize the common proposed key-management techniques with a suitable critique showing their pros and cons. Fourth, we introduce the effective paradigms of authentication in the state of the art. Fifth, the common two tools for verifying the security and integrity of protocols are presented. Sixth, the relevant research challenges are addressed to achieve trusted smart grids and protect their SMs against attack manipulations and unauthorized entities with a future vision. Accordingly, this survey can facilitate the efforts exerted by interested researchers in this regard. [ABSTRACT FROM AUTHOR]

The need for a low-cost interface between the grid and small (<250 kW) renewable distributed energy resources (DERs) is growing in importance as the number of small DERs continues to grow. In this study, a system architecture was proposed to investigate paths to an affordable interconnection for small renewable DERs.Then, a low-cost communication interface between a smart meter and smart inverter was installed using a commercially available bridge device. The interface device was selected based on an assessment concluding that it would be able to support the emerging advanced metering infrastructure (AMI) network. Next, messages were passed across the experimental end-to-end communication interface to test their speed and reliability. Success was based on whether the key functions defined in the standard IEEE 2030.5 were executed or not, which include set points, disconnect/reconnect, and Volt-VAr optimization. The results of the testing provided detailed insights into the benefits and limitations of the proposed architecture. Intermittency of weather-dependent DERs (e.g., solar and wind) adversely impacts the power quality of a DER, making hourly day-ahead prediction nearly impossible. With this in mind, the investigation also considered the potential of using smart inverter functions to reduce DER's intermittency. [ABSTRACT FROM AUTHOR]

Living and working in comfort while a building's energy consumption is kept under control requires monitoring a system's consumption to optimize the energy performance. The way energy is generally used is often far from optimal, which requires the use of smart meters that can record the energy consumption and communicate the information to an energy manager who can analyze the consumption behavior, monitor, and optimize energy performance. Given that the heating, ventilation, and air conditioning (HVAC) systems are the largest electricity consumers in buildings, this paper discusses the importance of incorporating occupancy data in the energy efficiency analysis and unveils energy inefficiencies in the way the system operates. This paper uses 1-year data of a highly efficient certified office building located in the Houston area and shows the power of self-organizing maps and data analysis in identifying up to 4.6% possible savings in energy. The use of time series analysis and machine-learning techniques is conducive to helping energy managers discover more energy savings. [ABSTRACT FROM AUTHOR]

This paper aims to understand the issues related to the perceived negative health effects of electromagnetic field (emf) radiation due to smart devices from the consumer's perspective. With the recent technological advancement, more and more devices like smartphones, smart locks, smart TVs, smart meters, etc., are becoming integral parts of human lives. On the one hand, these devices have added comfort and efficiency, while on the other hand, the negative health impacts of persistent exposure to emf radiation from these devices can't be denied. This paper used smart meter as an example of a smart device to explore potential adverse health effects of emf radiation by applying Soft System Methodology (SSM). Semi-structured interviews and workshops were conducted to gain insight into the research domain. The objective hierarchy has been proposed as a result of applying SSM. Some key concerns that must be considered for addressing consumers' health concerns were identified as the outcome of the conducted SSM workshops. The proposed hierarchy suggests creating a risk-management team of knowledgeable individuals and provides a roadmap for executives, government officials and policymakers. [ABSTRACT FROM AUTHOR]

Load forecasting is one of the main concerns for power utility companies. It plays a significant role in planning decisions, scheduling, operations, pricing, customer satisfaction, and system security. This helps smart utility companies deliver services more efficiently and analyze their operations in a way that can help optimize performance. In this paper, we propose a study of different techniques: multiple linear regression (MLR), random forests (RF), artificial neural networks (ANNs), and automatic regression integrated moving average (ARIMA). This study used electricity consumption data from Dubai. The main objective was to determine the load demand for the next month in the whole country and different municipal areas in Dubai, as well as to assist a utility company in future system scaling by adding new power stations for high-demand regions. The results showed that the accuracy of using ARIMA was about 93% when working with only a single district, but both ANN and RF achieved excellent accuracy of about 97% in all cases. In addition, the mean absolute percentage errors improved from 2.77 and 2.17 to 0.31 and 0.157 for ANN and RF, respectively, after anomaly elimination and the use of our proposal. Therefore, the use of an ANN for such data types is recommended in most cases, particularly when working on a complete dataset. Additionally, both the ANN and RF models are good choices when working on a single-category region because they both attained the same accuracy of almost 91.02 percent. [ABSTRACT FROM AUTHOR]

Many tasks that require a large workforce are automated. In many areas of the world, the consumption of utilities, such as electricity, gas and water, is monitored by meters that need to be read by humans. The reading of such meters requires the presence of an employee or a representative of the utility provider. Automatic meter reading is crucial in the implementation of smart grids. For this reason, with the aim to boost the implementation of the smart grid paradigm, in this paper, we propose a method aimed to automatically read digits from a dial meter. In detail, the proposed method aims to localise the dial meter from an image, to detect the digits and to classify the digits. Deep learning is exploited, and, in particular, the YOLOv5s model is considered for the localisation of digits and for their recognition. An experimental real-world case study is presented to confirm the effectiveness of the proposed method for automatic digit localisation recognition from dial meters. [ABSTRACT FROM AUTHOR]

This paper aims to construct an experimental system using the Arduino UNO microcontroller board to measure the electrical quantities and protect against surge circumstances in a single-phase power supply. The system utilized a voltage sensor and a current sensor, and data was transferred to a personal computer for graphical examination. The Root Mean Square (RMS) method was used to measure electrical values, and the system is allowed to decide whether to turn the load on or off based on the comparison of RMS voltage measurement to the lowest and maximum voltages. The Telemetry Viewer v0.5 software was used to create the monitoring interface, which monitored the system's RMS voltage, RMS current, active power, and trip signal. [ABSTRACT FROM AUTHOR]

Smart grid operations require accurate information on network topology and electrical equipment parameters. This paper proposes estimating such information with data from the smart grid. Assuming that the availability of bus voltage data is restricted to their magnitude, a linear model of the relationship between these data and the parameters of the admittance matrix is derived in a way that does not involve bus voltage angles. A regression optimizer is then proposed to minimize the deviation between data and values estimated by the linear model. Results on the IEEE 33 bus system are presented to illustrate the model accuracy and efficiency when used to estimate parameters of medium-voltage, three-phase balanced grids. [ABSTRACT FROM AUTHOR]

The smart meter has become necessary technology due to the high demand for energy-relevant real-time data to monitor and control the smart grids. According to UN-SDG 7. b, smart meter plays a vital role in infrastructure expansion and technological upgrades to ensure affordable and sustainable energy services in developing and underdeveloped countries. Although the smart metering approach yields many benefits, it involves complete infrastructural changes on utility providers and consumer premises. Meanwhile, the research society widely concentrates on replacing existing metering infrastructure with the new smart meter. But this changeover results in uneconomical large-scale smart meter deployments, including communications infrastructures and maintenance costs. Unfortunately, very few researches were conducted on utilizing the existing metering infrastructure to achieve the benefits of smart metering. And also, the viability of such an implementable model is less common. Aside from this, as advanced metering infrastructure emerges, smart grid communication is experiencing an increase in data traffic due to the collection of huge volumes of data. Consequently, this paper proposes a Network Enabled Smart Energy Meter that can network existing multifunctional digital energy meters without replacing them for smart meters, while reducing network traffic through a decentralized framework. The proposed design is simulated and tested in Proteus software to validate basic and decentralized data processing operations. Obtained results show that without changing metering infrastructure at consumer premises, smart metering can be incorporated using Network Enabled Smart Energy Meter with lower deployment cost for a group of houses compared with existing metering technologies. Moreover, decentralized data processing frameworks are used to reduce data traffic in smart grid communication networks, and their results are discussed in the comparative data volume analysis section. In addition, we conducted a residential consumer preference survey to validate the acceptance rate of the proposed technology for low-cost smart meter. [ABSTRACT FROM AUTHOR]

Analytics is an essential procedure to acquire knowledge and support applications for determining electricity consumption in smart homes. Electricity variables measured by the smart meter (SM) produce a significant amount of data on consumers, making the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analysing the so-called big data possible. The monitoring and visualization of information aid in personalizing applications that benefit both homeowners and researchers in analysing consumer profiles. This paper presents a smart meter for household (SMH) to obtain load profiles and a new platform that allows the innovative analysis of captured Internet of Things data from smart homes, photovoltaics, and electrical vehicles. We propose the use of cloud systems to enable data-based services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. The requirements and system design components are discussed. [ABSTRACT FROM AUTHOR]

• This research proposes a multifactor index system for evaluating the operating state of smart meters. • An improved combinatorial weighting method is used to estimate the weight of factors. • The research concludes that the proposed method can contribute to improve the efficiency of smart meter detection. As an important terminal of smart grid, smart meter has the functions of equipment health monitoring and power metering. However, it is difficult to quantify the impact of factors such as ex-factory parameters, historical failure rates, electromagnetic interference when conducting an operational condition assessment. Therefore, in view of the large number of influencing factors and the difficulty in quantifying the degree of influence, an improved multifactor evaluation model for the operating status of smart meters is constructed. Firstly, a multifactor index system for evaluating the operating state of smart meters is constructed by analyzing the working mechanism and operating characteristics of smart meters. Then, an improved combinatorial weighting method is proposed by combining the subjective weight and the objective weight. The combinatorial weighting method is used to estimate the weight of factors in the health status evaluation of smart meters, and the improved multifactor evaluation method is presented to evaluate the health status of smart meters. Finally, an example is given to verify and analyze the proposed method. Compared with the other three methods, this method can provide effective evaluation results, and help guide targeted maintenance or replacement to improve the efficiency of smart meter detection. [ABSTRACT FROM AUTHOR]

Smart meters are the primary source of energy consumption data in the smart grid network, which can record energy usage with fine granularity. The use of smart meters expands the interaction between the energy supplier and the consumer. Security for smart meters and privacy for the users are, therefore, paramount. The study of smart-meter data, particularly security concerns, is a very active research area. A high-frequency smart meter captures and transmits energy usage data in small bursts (per second or minute). Maintaining a high level of security while processing data in such a short period of time is critical for resource-limited devices like smart meters. To address this issue, this work presents a privacy-preserving protocol for high-frequency smart meters (P3HF) using difference expansion-based reversible watermarking and Paillier homomorphic encryption. The proposed protocol significantly increases the security of high-frequency smart meters by introducing a unique encryption server and using joint watermarking and encryption to protect real-time data transmission. The acquired results, which include experiments conducted on a real hardware platform using Arduino UNO Rev.3, show that the proposed scheme ensures security and user data privacy while consuming a low amount of energy and time for execution. Additionally, a comparative analysis demonstrates that the proposed protocol performs better than earlier research works concerning requirements for data and privacy, resilience to possible attacks, and capacity to overcome their limitations. [ABSTRACT FROM AUTHOR]