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With the popularity of smart meters, the frequent information exchange between smart grids and consumers leads to easy leakage of consumers' electricity consumption data. These leaked electricity consumption data are obtained by some malicious attackers and used to infer consumers' behavioural patterns by non‐intrusive load monitoring (NILM), which seriously threatens consumers' privacy. Therefore, the multi‐agent Hidden Markov energy management model is proposed in this paper to safeguard the privacy of consumers. First, a weighted Bayesian risk model is proposed, which combines privacy leakage risks and energy storage system (ESS) losses in a microgrid with multiple agents. Next, a three‐loop model for lithium batteries is constructed to quantify the capacity degradation and cost issues of the ESS. Finally, the multi‐objective optimization problem is resolved by integrating the Bayesian risk model with a hidden Markov model to simulate attackers. The proposed multi‐agent Markov decision process method is validated on Electricity Consumption and Occupancy (ECO) dataset, and control strategies are evaluated based on different weights in the Bayesian risk model. The results demonstrate that by incorporating the multi‐agent approach and energy storage system capacity degradation into the privacy protection strategy, the lifespan of the energy storage system can be significantly increased. [ABSTRACT FROM AUTHOR]

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]

Given the massive deployment of smart meters at international level, it is necessary to develop methodologies to extract knowledge from the data that they can provide. To this end, it is necessary to associate energy, socio-demographic and/or technical-constructive data, because this is the only way to identify profiles with their corresponding relevant variables or drivers. The usual problem is that socio-technical information about users is limited or non-existent, as it is costly to collect. Consequently, this work presents as a novelty the use of census information to characterize groups of urban segments with similar daily electricity load curves, which avoids the need to collect socio-technical information through specific surveys or direct measurements. In this way, relevant variables are identified in the determination of consumption patterns in the study case (Montevideo-Uruguay) and they are used to infer the daily behavior of those sectors of the city that don't have this information. [ABSTRACT FROM AUTHOR]

The ubiquitous smart meters are expected to be a central feature of future smart grids because they enable the collection of massive amounts of fine-grained consumption data to support demand-side flexibility. However, current smart meters are not smart enough. They can only perform basic data collection and communication functions and cannot carry out on-device intelligent data analytics due to hardware constraints in terms of memory, computation, and communication capacity. Moreover, privacy concerns have hindered the utilization of data from distributed smart meters. Here, we present an end-edge-cloud federated split learning framework to enable collaborative model training on resource-constrained smart meters with the assistance of edge and cloud servers in a resource-efficient and privacy-enhancing manner. The proposed method is validated on a hardware platform to conduct building and household load forecasting on smart meters that only have 192 KB of static random-access memory (SRAM). We show that the proposed method can reduce the memory footprint by 95.5%, the training time by 94.8%, and the communication burden by 50% under the distributed learning framework and can achieve comparable or superior forecasting accuracy to that of conventional methods trained on high-capacity servers. Smart meters collect detailed consumption data but struggle with on-device analytics due to hardware and privacy issues. The authors propose an end-edge-cloud federated split learning framework to introduce edge intelligence, reducing memory, training time, and communication burden while maintaining accuracy. [ABSTRACT FROM AUTHOR]

The advanced metering infrastructure (AMI) of the smart grid plays a critical role in energy management and billing by enabling the periodic transmission of consumers' power consumption readings. To optimize data collection efficiency, AMI employs a "change and transmit" (CAT) approach. This approach ensures that readings are only transmitted when there is enough change in consumption, thereby reducing data traffic. Despite the benefits of this approach, it faces security challenges where malicious consumers can manipulate their readings to launch cyberattacks for electricity theft, allowing them to illegally reduce their bills. While this challenge has been addressed for supervised learning CAT settings, it remains insufficiently addressed in unsupervised learning settings. Moreover, due to the distortion introduced in the power consumption readings due to using the CAT approach, the accurate prediction of future consumption for energy management is a challenge. In this paper, we propose a two-stage approach to predict future readings and detect electricity theft in the smart grid while optimizing data collection using the CAT approach. For the first stage, we developed a predictor that is trained exclusively on benign CAT power consumption readings, and the output of the predictor is the actual readings. To enhance the prediction accuracy, we propose a cluster-based predictor that groups consumers into clusters with similar consumption patterns, and a dedicated predictor is trained for each cluster. For the second stage, we trained an autoencoder and a one-class support vector machine (SVM) on the benign reconstruction errors of the predictor to classify instances of electricity theft. We conducted comprehensive experiments to assess the effectiveness of our proposed approach. The experimental results indicate that the prediction error is very small and the accuracy of detection of the electricity theft attacks is high. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel methodology for estimating missing data in energy consumption datasets. Conventional data imputation methods are not suitable for these datasets, because they are time series with special characteristics and because, for some applications, it is quite important to preserve the shape of the daily energy profile. Our answer to this need is the use of autoencoders. First, we split the problem into two subproblems: how to estimate the total amount of daily energy, and how to estimate the shape of the daily energy profile. We encode the shape as a new feature that can be modeled and predicted using autoencoders. In this way, the problem of imputation of profile data are reduced to two relatively simple problems on which conventional methods can be applied. However, the two predictions are related, so special care should be taken when reconstructing the profile. We show that, as a result, our data imputation methodology produces plausible profiles where other methods fail. We tested it on a highly corrupted dataset, outperforming conventional methods by a factor of 3.7. [ABSTRACT FROM AUTHOR]

Accurate energy consumption forecasting is essential in the decision-making process, and in optimizing energy production and distribution to meet customers' demands, especially given the fluctuating demand. The widespread deployment of smart meters has revolutionized the collection of time-series data on energy consumption, providing detailed insights into usage patterns at a granular level. This paper presents a comprehensive comparison of eight time-series forecasting models: Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA), Decision Trees (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-TermMemory networks (LSTM) to assess the most efficient model on the smart meters' dataset. The models are evaluated using several statistical metrics, and based on the experimental results analysis, the LSTM model provided the best prediction performance with an RMSE of 2.106, a MAPE of 0.19, and a MAE of 1.599. [ABSTRACT FROM AUTHOR]

The advancement of smart metering technology is progressing steadily and inevitably across various key economic sectors. The utilizatio.n of remote sensors in agriculture presents unique characteristics and specific challenges. In this study, an on-site experiment was carried out on a Slovakian production farm to analyze the transition from traditional measurement methods to smart meters, focusing on timing decisions related to soil preparation and sowing and their relation to scientifically justified dates. Consequently, a clear distinction was observed in terms of the timing decisions made regarding agricultural activities during traditional, combined, and scientifically based approaches in meteorological data readings. This study contrasts these three scenarios and deliberates on the factors that need to be carefully evaluated before incorporating remote sensors into agricultural processes. This study serves as a valuable resource for individuals involved in the adoption of smart metering practices in the Eastern European agricultural sector and promotes an improved understanding of the interactions within smart-sensing, scientific developments, and land management that contribute to the goal of land-system sustainability. [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]

Optimizing energy consumption is an important aspect of industrial competitiveness, as it directly impacts operational efficiency, cost reduction, and sustainability goals. In this context, anomaly detection (AD) becomes a valuable methodology, as it supports maintenance activities in the manufacturing sector, allowing for early intervention to prevent energy waste and maintain optimal performance. Here, an AD-based method is proposed and studied to support energy-saving predictive maintenance of production lines using time series acquired directly from the field. This paper proposes a deep echo state network (DeepESN)-based method for anomaly detection by analyzing energy consumption data sets from production lines. Compared with traditional prediction methods, such as recurrent neural networks with long short-term memory (LSTM), although both models show similar time series trends, the DeepESN-based method studied here appears to have some advantages, such as timelier error detection and higher prediction accuracy. In addition, the DeepESN-based method has been shown to be more accurate in predicting the occurrence of failure. The proposed solution has been extensively tested in a real-world pilot case consisting of an automated metal filter production line equipped with industrial smart meters to acquire energy data during production phases; the time series, composed of 88 variables associated with energy parameters, was then processed using the techniques introduced earlier. The results show that our method enables earlier error detection and achieves higher prediction accuracy when running on an edge device. [ABSTRACT FROM AUTHOR]

In the power industry, advanced techniques have furthered the development of the smart grid's power system and management. The world's third-largest country is India, which has a producer and consumer of electricity, is struggling with different power-related problems, as well as distribution losses, transmission, environmental concerns, and electricity theft. The energy sector is investigating innovative technologies to enhance grid efficiency, security, and sustainability to address power-related issues. Recently, smart grid technology has ascribed significance to the energy scenario; the term "smart grid" relates to electric electricity. The study aims to thoroughly evaluate how smart grid technologies might improve the reliability and efficiency of India's electrical system. This article examines the impact of smart grid technologies on national grids and makes some proposals to authorities for switching their traditional grid system to a smart grid system. The results indicate the yearly wind profile, comparative analysis of energy consumption, and cost analysis of the system. Smart grid integration is strengthened by the useful insights provided by the annual wind profile study, which reveals the region's renewable energy potential. Analysis of costs and energy consumption patterns show that switching to a smart grid system is financially feasible in the long term, and studies of impacts on utilization of resources show that it is beneficial. [ABSTRACT FROM AUTHOR]

Highlights: A method to investigate water consumption based on pressure data is developed. The method exploits the headloss-flowrate equation to obtain water-consumption data. The method is validated on a real case study, resulting in an average error of 2.3%. Limitations affecting the installation of domestic flow meters are overcome. Insights into the features of individual water-consumption events are provided. In the era of digital transformation of water distribution networks, an increasingly important role is played by smart metering technologies, which allow detailed characterization of water consumption up to the end-use (i.e., domestic-fixture) level. To this end, smart flow meters make the collection of water-consumption data at high temporal resolution possible, but their installation can be unfeasible due to technical and economic limitations. As an alternative to the traditional flow-measurement-based methods for end-use characterization, a pragmatic method to obtain information about end-use water consumption exclusively based on pressure data is proposed in this study. In particular, a dual-phase methodology is developed, exploiting (i) pressure data collected at two sections of the user's inlet pipeline and (ii) the pressure-flowrate relationship to discriminate between internal and external water-use events and estimate the household water-consumption time series, which is then subjected to individual-event analysis. The results obtained on a real case study undergone to 1-s resolution pressure monitoring over about one month and a half confirm the method's effectiveness in obtaining the flowrate time series with an average error of about 2.3% and successfully identifying water-consumption events along with their features. [ABSTRACT FROM AUTHOR]

This research uses data analysis and mining techniques to determine the technological expansion of measurement systems in a public service company. It integrates technical, economic, geographic, and social variables into the analysis using machine learning techniques to discover patterns and relationships in large data sets. The fuzzy logic methodology is applied using the MATLAB programming tool "Fuzzy Logic" to build algorithms that allow for the correct selection of measurement, achieving greater efficiency and precision in the assignment of meter types. The results show that 98% of the metering systems in the significant part are electronic meters, with the "Residential BT" rate being the most extensive data set. Implementing the "fuzzy logic" technique recognizes that more than 60% of the meters are electronic, with the registration of active energy, reactive energy, and demand, allowing for greater control over the marketing variables of the distribution system operator. This research suggests that a future restructuring of electrical metering systems benefits the company and its users. By applying the analysis, a portfolio of viable projects for the replacement of measurement systems is obtained, and they are grouped into two clusters based on the total cost of the technological change. [ABSTRACT FROM AUTHOR]

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format. [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) is a promising technology that combines communication and data networking. Integration of Smart Grids (SGs) and IoT will fulfill an increased demand for energy requirements by transforming the reliable and efficient traditional power grids. The SG enables bi-directional transmission between the Service Provider (SP) and Smart Meter (SM) to send and receive information regarding electricity consumption over a public channel. The public channel allows an adversary to intercept the information exchanged between them and tamper with the SM as it is installed outside which leads to forging or modification of the messages and privacy violation. In addition, the SM has limited computational and storage capacity. To protect SM privacy and securely communicate in the SG network, Physically Unclonable Functions (PUFs) based Authentication and Key Agreement (AKA) schemes were suggested in the literature. However, they may suffer from the machine learning modeling attack and several other security issues. Thus, we propose a finite state machine enabled controlled PUF based AKA (PF-AKA) Framework for the IoT based SG (IoT-SG) network. The PF-AKA framework is verified formally using the Real-or-Random (RoR) model, AVISPA tool, and BAN logic. It shows that PF-AKA achieves the security requirements along with protection from the SM physical and modeling attacks. The performance analysis is carried out and it represents that the PF-AKA yields competitive computation and communication costs compared to AKA schemes in the literature for the IoT-SG network. [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]

Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones. [Display omitted] • Encryption-based data consolidation strategy for 5G in fog computing is presented. • Data consolidation and data extraction algorithm at fog devices and cloud servers. • A comparative analysis based on storage, communication and transmission cost is done. [ABSTRACT FROM AUTHOR]

This paper presents the application of the Comparison of Flow Pattern Distribution (CFPD) method for detecting water leakage and understanding consumption behaviors at both microscale and macroscale. Implemented at Lille University's Scientific Campus, this research utilizes Automated Meter Reading (AMR) to collect real-time water supply and consumption data. The research successfully identified several significant leak events by analyzing this data with the CFPD method on weekly and daily scales. The analysis of the data resulted in identifying the seasonal and operational consumption patterns across different periods of the year. The findings highlight the effectiveness of the CFPD method in achieving water conservation and operational efficiency, consequently contributing to the UN Sustainable Development Goal (SDG) 6 concerning clean water and sanitation. [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]

In response to the practical application of the electricity consumption information collection system in the online monitoring business of measuring equipment, the author introduces a method for analyzing the abnormal flying away of electricity meters based on the IoT technology LOF local anomaly detection algorithm. This method can effectively determine whether the abnormal energy representation value belongs to accidental or trend anomalies by calculating the abnormal factor of the energy representation value. After excluding the influence of accidental data, perform a secondary judgment on the abnormal flight of the energy meter. The experimental results show that when calculating the LOF factor of the electricity meter, it can be found that the LOF curve data range is mainly concentrated in the range of 0.8 to 1.3, and there is no significant change in the LOF factor near the mutation point. This proves that this method can effectively improve the accuracy of anomaly detection, avoid misjudgment of faults, and improve the efficiency of on-site fault handling. [ABSTRACT FROM AUTHOR]

In a smart grid, collected electricity consumption periodically from smart meters allow entities to bill the customers, power company to operate the grid successfully, and users to control the use of their appliances. However, energy consumptions of users should be protected since the data provides the user's daily habit that an adversary uses the data to extract useful information about the users. Moreover, users' identities should not be disclosed to untrusted entities since the untrusted entities map identities to their real identities. In this paper, we propose a system that protects users' data privacy using multi-pseudorandom identities and a randomization technique. Moreover, the proposed work provides fast authentication for smart meters to send their readings to data aggregators. Furthermore, the proposed work is based on consortium blockchain to eliminate a single point of failure and provides transparency of messages and operations. In addition, we use dynamic billing and pricing mechanism for the users to see their bills. [ABSTRACT FROM AUTHOR]

This paper investigates whether informative feedback on consumption can nudge water saving. We launched a five-month online information campaign which involved around 1,000 households located in the province of Milan (Italy) with a smart meter. A group of households received monthly reports via email on their per capita daily average water consumption, including a social comparison component. The Intention to Treat (ITT) analysis shows that, compared to a benchmark group, the units exposed to the intervention reduced their per capita water consumption by around 6% (25.8 liters per day or 6.8 gallons). Being able to observe the email opening rate, we find that the ITT effect is mainly driven by complying units. Through an Instrumental Variable approach, we estimated a Local Average Treatment Effect equal to 54.9 liters per day of water saving. A further Regression Discontinuity Design analysis shows that different feedback on consumption class size differentially affected water saving at the margin. We also found that the additional water saving increased with the number of monthly reports, though it did not persist two months after the campaign expired. [ABSTRACT FROM AUTHOR]

This research investigates dynamic pricing as a demand management tool to reduce cost and increase the lifespan of water distribution systems by reducing peak hour demand. Individual consumer responses to changes in hourly water price are simulated using advanced metering infrastructure (AMI) data. Demand profiles are used as input to a hydraulic simulation model to evaluate the effects of changing demands on flows and in‐network metrics. The framework is applied to Lakewood City, California, using a model of the pipe network and AMI data collected at nearly 20,000 accounts. Four dynamic pricing policies are applied to the model to show that reductions in morning peak demand ranging from 6% to 25% reduce peak energy demands up to 14%. These small changes in overall energy demand, up to a 1.7% reduction, lead to relatively larger overall reductions in energy cost, up to 5.5%. The results demonstrate the importance of dynamic pricing as a demand‐side strategy for infrastructure management and highlight the potential to accommodate demand growth without additional infrastructure investments. Key Points: A dynamic pricing strategy for water can lower peak water demands, peak energy, and energy cost of water distribution systemsAdvanced metering infrastructure data and hydraulic models are used to apply and assess dynamic pricing modelsPotential growth in water demand can be accommodated by existing infrastructure capacity through dynamic pricing [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]

This article examines the fragmented connections of Jordan's smart grid, building on scholarship that questions how smart infrastructures reshape governance, sociospatial exclusion, and the fabric of urban life. Jordan's ambitious smart energy program is often held up as a global model by investors, as it catalyzed over US$4 billion in private investment for new renewable and smart energy development. Yet smart energy transition is experienced in powerfully uneven ways, as distributed solar installations and smart grid technologies radically remake the spaces of urban life. Rooted in sixteen months of ethnographic fieldwork, this article traces the vertical materialization of the smart grid from the ground up, from in-home smart meters through the evolving interconnections that they enact. I argue that (post)colonial property relations engender an archipelagic landscape of (dis)connectivity that redistributes the benefits and burdens of digitalization. Drawing from Glissant's archipelagic thought, I examine (dis)connection and urban fragmentation as a form of relation that links enduring (post)colonial relations to contemporary projects of smart development. In the (post)colonial world, as smart infrastructures are built into the conduits of uneven property relations, they come to incorporate not only capitalist logics but also racialized logics and historically contingent relations of exclusion and differentiation. [ABSTRACT FROM AUTHOR]

The increasing utilisation of the distribution grid caused by the ramp-up of electromobility and additional electrification can be eased with flexibility through smart and bidirectional charging use cases. Implementing market-oriented, grid-, and system-serving use cases must be tailored to the different national framework conditions, both in technical and regulatory terms. This paper sets out an evaluation methodology for assessing the implementation of smart and bidirectional charging use cases in different countries. Nine use cases are considered, and influencing factors are identified. The evaluation methodology and detailed analysis are applied to Austria, the Czech Republic, Denmark, Finland, France, Germany, Italy, the Netherlands, Spain, and Sweden. In every country, the implementation of vehicle-to-home use cases is possible. Realising market-oriented use cases is feasible in countries with a completed smart meter rollout and availability of tariffs with real-time pricing. Grid-serving and ancillary service use cases depend most on country-specific regulation, which is why no clear trend can be identified. Use cases that require direct remote controllability are the most distant from implementation. The overarching analysis provides orientation for the design of transnational products and research and can serve as a basis for a harmonisation process in regulation. [ABSTRACT FROM AUTHOR]

The importance of urban water supply systems and public services is globally recognized. Nonrevenue water directly affects a water utility's economic, financial, and environmental sustainability. In Portugal, the mean of the nonrevenue water for the distribution systems corresponded to 28.8% in 2019. Smart metering technology is crucial for consumption monitoring and enhancing apparent and real loss network management (e.g., water meters' global error evaluation, detection of illegal uses, and real loss estimation through the minimum night flow analysis). However, this technology is expensive in acquisition, installation, operation, and maintenance. This study aims to support water utilities in inferring the total consumption using a representative sample of customers with smart meters instead of smart metering data from all customers. A stratified sampling was considered using only the customers' billing time series for the strata definition. A predominantly domestic zone was used, and eight strata were obtained with a clustering analysis [temporal correlation (CORT) dissimilarity and Ward method]. Stratified sampling was applied to minimize the variance of the total water consumption estimator. A representative sample of 259 dimensions (53%) was chosen to infer, with small errors, essential consumption statistics for water utilities: total consumption (with an error of 0.12%), total consumption time series, water consumption patterns, minimum night consumption, and volume distribution by the flow rate. The successful outcomes obtained were crucial in supporting the proposed methodology. This study has provided evidence that installing smart meters for all consumers in a distribution network area is not necessary to acquire accurate and meaningful consumption information crucial for effective network management and water loss control. Moreover, using only billing data to perform the sample selection of consumers is useful for water utilities, because they may face difficulties obtaining extra consumer information. [ABSTRACT FROM AUTHOR]

Narrowband Power Line Communication (NB-PLC) involves transmitting data by overlaying a high frequency low amplitude signal (ranging from 9 kHz to 500 kHz) onto the low-frequency high amplitude signal (50 Hz to 60 Hz) of the power grid. While using the existing power grid for communication is convenient, it was not originally designed for this purpose, leading to challenges such as conducted emissions and infrastructure constraints. To overcome these technical obstacles, power line filters (PLFs) are a viable solution. The results of our research work, focusing on the optimization of PLFs for NB-PLC to ensure their design fits the needed use case while avoiding over-engineering, are presented in this article. Our study concentrates specifically on the filtering of PLC signal and conducted emissions up to 500 kHz. Building upon a PLC PLF extensively discussed in our previous work—which blocks the PLC signal in the CENELEC-A frequency band regardless of its placement within the electrical installation, sometimes leading to over-engineering—this research aims to adapt the filter order and components for a variety of real scenarios in CENELEC-A, FCC, and ARIB frequency bands. By characterizing different filters, our work provides tailored solutions for these scenarios and serves as a framework for future filter designs in PLC applications. [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]

This document provides an overview of the advancements in energy harvesting technologies and their integration into the Internet of Things (IoT) and wireless sensor networks. It includes 31 papers covering various energy harvesting devices, optimization techniques, and potential applications. The research aims to improve the efficiency and sustainability of wireless sensor networks while minimizing energy consumption. The articles explore different methods of energy harvesting, such as solar, thermal, vibrational, and radio frequency energy, and discuss their application in various fields, including wearable devices, solar energy harvesting, and IoT-enabled smart meters. Overall, this collection offers a comprehensive overview of the current research and advancements in energy harvesting for sensor networks. [Extracted from the article]

The increasing threat of cyber-attacks on modern power systems highlights the need for a comprehensive examination through simulations. This study conducts an in-depth simulation of cyber-attacks on critical smart grid components, including smart meters, substation automation, and battery management systems, to expose and analyze potential disruptions to power system operations. We identify vulnerabilities that can lead to severe grid instabilities, such as voltage variations, system collapses, and inverter failures. Our analysis underscores the complex interactions between cyber threats and grid components, revealing how disruptions extend beyond mere load interruptions to affect the core infrastructure. We advocate for integrating established cybersecurity frameworks like NIST, ISO/IEC 27001, and IEC 62443, essential in fortifying grid stability against these dynamic threats. Our findings highlight the urgent need for continuous adaptation and enforcement of these frameworks to enhance resilience and ensure the reliability of modern power grids against cyber-attacks. [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]

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications. [ABSTRACT FROM AUTHOR]

The galloping of transmission lines (GTLs) has a significant impact on the development of smart grids. However, traditional vibration frequency monitoring devices for transmission lines suffer from issues such as low measurement accuracy, high environmental requirements, and the inability to achieve self‐powering. A self‐powered vibration frequency monitoring method is proposed based on frequency‐sensing triboelectric nanogenerator (F‐TENG) and micro thermoelectric generators (MTEG). Models for vibration frequency sensing based on F‐TENG and energy capture based on MTEG are established. A flexible self‐powered sensing prototype, integrating F‐TENG, MTEG, and a Signal processing and Energy harvesting Circuit (SEC), is fabricated. Additionally, an innovative solvothermal method for the preparation of MTEG materials is presented, resulting in Bi2Te3‐based thermoelectric materials with significantly high thermoelectric conversion performance. Experimental results demonstrate that within the range of 0.1–5.1 Hz, F‐TENG can precisely perceive the frequency of GTLs, with a maximum error of 1.274%. MTEG achieves a maximal open‐circuit voltage of 3.282 V. Finally, the SEC unit is designed to couple the outputs of F‐TENG and MTEG for frequency calculation and wireless transmission to a microcontroller. This device provides an efficient solution for monitoring the frequency of GTLs and offers robust support for the stability of the smart grid. [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]

Dynamic energy contracts, offering hourly varying day-ahead prices for electricity, create opportunities for a residential Battery Energy Storage System (BESS) to not just optimize the self-consumption of solar energy but also capitalize on price differences. This work examines the financial potential and impact on the self-consumption of a residential BESS that is controlled based on these dynamic energy prices for PV-equipped households in the Netherlands, where this novel type of contract is available. Currently, due to the Dutch Net Metering arrangement (NM) for PV panels, there is no financial incentive to increase self-consumption, but policy shifts are debated, affecting the potential profitability of a BESS. In the current situation, the recently proposed NM phase-out and the general case without NM are studied using linear programming to derive optimal control strategies for these scenarios. These are used to assess BESS profitability in the latter cases combined with 15 min smart meter data of 225 Dutch households to study variations in profitability between households. It follows that these variations are linked to annual electricity demand and feed-in pre-BESS-installation. A residential BESS that is controlled based on day-ahead prices is currently not generally profitable under any of these circumstances: Under NM, the maximum possible annual yield for a 5 kWh/3.68 kW BESS with day-ahead prices as in 2023 is EUR 190, while in the absence of NM, the annual yield per household ranges from EUR 93 to EUR 300. The proposed NM phase-out limits the BESS's profitability compared to the removal of NM. [ABSTRACT FROM AUTHOR]

With the surge in population growth, the demand for electricity has escalated, necessitating efficient solutions to enhance the reliability and security of electrical systems. Smart grids, functioning as self-sufficient systems, offer a promising avenue by facilitating bi-directional communication between producers and consumers. Ensuring the stability and predictability of smart grid operations is paramount to evaluating their efficacy and usability. Machine learning emerges as a crucial tool for decision-making amidst fluctuating consumer demand and power supplies, thereby bolstering the stability and reliability of smart grids. This study explores the performance of various machine learning classifiers in predicting the stability of smart grid systems. Utilizing a smart grid dataset obtained from the University of California's machine learning repository, classifiers such as logistic regression (LR), XGBoost, linear support vector machine (Linear SVM), and SVM with radial basis function (SVM-RBF) were evaluated. Evaluation metrics, including accuracy, precision, recall, and F1 score, were employed to assess classifier performance. The results demonstrate high accuracy across all models, with the Deep Neural Network (DNN) model achieving the highest accuracy of 99.5%. Additionally, LR, linear SVM, and SVM-RBF exhibited comparable accuracy levels of 98.9%, highlighting their efficacy in smart grid stability prediction. These findings underscore the utility of machine learning techniques in enhancing the reliability and efficiency of smart grid systems. [ABSTRACT FROM AUTHOR]

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

Background: Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly. Scope and Methodology: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents' daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people's care. [ABSTRACT FROM AUTHOR]

This study presents the measurements of exposure to electromagnetic fields, carried out comparatively following standard methods from fixed sites using a broadband meter and using a smartphone on which an App designed for this purpose has been installed. The results of two measurement campaigns carried out on the campus of the University of Alcalá over an area of 1.9 km2 are presented. To characterize the exposure, 20 fixed points were measured in the first case and 860 points along the route made with a bicycle in the last case. The results obtained indicate that there is proportionality between the two methods, making it possible to use the smartphone for comparative measurements. The presented methodology makes it possible to characterize the exposure in the area under study in four times less time than that required with the traditional methodology. [ABSTRACT FROM AUTHOR]

Smart grids integrate information technology, decision support systems, communication networks, and sensing technologies. All these components cooperate to facilitate dynamic power adjustments based on received client consumption reports. Although this brings forth energy efficiency, the transmission of sensitive data over the public internet exposes these networks to numerous attacks. To this end, numerous security solutions have been presented recently. Most of these techniques deploy conventional cryptographic systems such as public key infrastructure, blockchains, and physically unclonable functions that have either performance or security issues. In this paper, a fairly efficient authentication scheme is developed and analyzed. Its formal security analysis is carried out using the Burrows–Abadi–Needham (BAN) logic, which shows that the session key negotiated is provably secure. We also execute a semantic security analysis of this protocol to demonstrate that it can resist typical smart grid attacks such as privileged insider, guessing, eavesdropping, and ephemeral secret leakages. Moreover, it has the lowest amount of computation costs and relatively lower communication overheads as well as storage costs. [ABSTRACT FROM AUTHOR]

With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes. [ABSTRACT FROM AUTHOR]

Considering the smart grids establishment, where the presence of nonlinear loads and new power generation sources become increasingly expressive, there is a potential for unknown disturbances occurrence. Besides, given the huge amount of data coming from smart meters, it is important to preserve the relevant information and promote efficient storage of the data. Novelty Detection (ND) techniques can be used to address these challenges. The Stockwell Transform (ST) is a time–frequency distribution that has shown a great ability to detect novelties related to stationarity changes in signals. It can be applied to voltage and current signals from the grid. Therefore, the main contribution of this paper is to provide an FPGA real-time implementation of an ST-based novelty detector. Novelty detection goes beyond classic electrical disturbances well known in the area of power quality, as it relates any novelty event to a stationarity change in the analyzed signal. To perform the implementation in FPGA platform, the use of a soft-core processor is proposed to optimize the hardware resources of the FPGA. In addition, a strategy of voice selection is proposed to reduce the complexity and execution time of the algorithm in real-time implementation, while maintaining the detection capability. The proposed ND technique was implemented in a low-cost FPGA-based functional prototype, where synthesized and field-recorded real signals were applied, attesting the ND performance. The reported tests show that the strategies adopted for real-time implementation provided an optimization of hardware resources and enabled the execution of the ST algorithm and determination of the novelties. [ABSTRACT FROM AUTHOR]

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Mobile ad hoc networks are self-organizing networks that do not rely on fixed infrastructure. Smart antennas employ advanced beamforming technology, enabling ultra-long-range directional transmission in wireless networks, which leads to lower power consumption and better utilization of spatial resources. The media access control (MAC) protocol design using smart antennas can lead to efficient usage of channel resources. However, during ultra-long-distance transmissions, there may be significant transport delays. In addition, when using the time division multiple access (TDMA) schemes, it can be difficult to manage conflicts arising from adjacent time slot advancement caused by latency compensation in ultra-long-range propagation. Directional transmission and reception can also cause interference between links that reuse the same time slot. This paper proposes a new distributed dynamic TDMA protocol called State Interaction-based Slot Allocation Protocol (SISAP) to address these issues. This protocol is based on slot states and includes TDMA frame structure, slot allocation process, interference self-avoidance strategy, and slot allocation algorithms. According to the simulation results, the MAC layer design scheme suggested in this paper can achieve ultra-long-distance transmission without conflicts. Additionally, it can reduce the interference between links while space multiplexing. Furthermore, the system exhibits remarkable performance in various network aspects, such as throughput and link delay. [ABSTRACT FROM AUTHOR]

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems. [ABSTRACT FROM AUTHOR]