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

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]

At present, a higher rate of power consumption is caused by intelligent grid applications. Due to high power consumption, the system efficiency, as well as the energy rate of the system, was high. So, to reduce the power consumption rate and increase the system's efficiency, a novel Ant Lion-based Auto Encoder System (ALbAES) was developed in this research. With this model, the power consumption reduced and the system's efficiency is increased, and the parameters of the proposed model were obtained in a better range. This work initially pre-processed the power demand data sets to remove the noisy data. The smart grid and the control and monitoring features were extracted through feature extraction. Those extracted features were used in further processes. Moreover, the fitness of the extracted features was compared with the ant lion fitness. The calculation was done based on the ant lion optimization and the Autoencoder. Ideally, the system's efficiency was increased as 96% based on the fitness function of developed optimization algorithm. To detect the performance of the proposed model, the parameters in the proposed model were compared with the other existing models. The system's efficiency was improved, the rate of power flow was reduced, and the energy rate of the model was reduced. The design was implemented in MATLAB software, and the results were executed on the windows ten platform. [ABSTRACT FROM AUTHOR]

In the present era of smart grids, accurate prediction of energy uses is becoming increasingly essential to guarantee optimal energy efficiency. This study contributes to the field by utilizing advanced machine learning techniques to perform predictions of energy consumption using the data from Internet of Things (IoT) devices. Specifically, the approach utilizes regression neural network (RNN) structures, such as long short-term memory (LSTM) and gated recurrent units (GRUs). The data from IoT sensors are more extensive and detailed than those of conventional smart meters, allowing for the development of more complex models of energy use patterns. This study utilizes Adam-optimized LSTM, RNN, and GRU models, along with stochastic gradient descent, to evaluate their performance in addressing the complexity of time-series data in energy forecasting on different network configurations. Result of the analysis indicates that LSTM models, which are run with the Adam optimizer, are more accurate in terms of predictions compared with the other models. This conclusion is supported the test results of these models that are within the lowest root mean square error and mean absolute error scores. All the models under the analysis exhibit signs of overfitting based on the performance indicators for the training and the testing data. This notion implies that the regularization should be utilized to ensure the improved generalizability of the models. These findings show that deep learning can have a lasting influence in improving energy consumption management systems to meet the sustainability and energy efficiency requirements. These observations are beneficial for the gradual improvements of smart grids. [ABSTRACT FROM AUTHOR]

This review underscores the escalating global research trend in this field since 2000. The primary scientific disciplines contributing to extensive research on smart water meters are engineering, computer science, and energy. In terms of countries, the analysis reveals that the United States, India, and China exhibit the highest scientific production. Concerning affiliations, prominent contributors include Griffith University, Politecnico di Milano, and the Università degli Studi di Salerno. Regarding worldwide research trends, an examination of distinct clusters defined by their principal keywords was conducted. The following clusters were identified in order of significance based on the number of publications: Urban Water Meters, IoT Connection, Communication and Security, Grid Management, Water Networks, Hot Water, Groundwater Monitoring, and Smart Irrigation. Of particular note is the growing use of machine learning applications, especially in the management of distribution networks. This trend opens up a promising avenue for addressing complex problems in real time. [ABSTRACT FROM AUTHOR]

Safeguarding the data collected by smart meters is essential because the disclosure of this information may threaten the privacy of the consumer. By obtaining them, hackers can find out the behavior of the person and use that information for malicious purposes. Therefore, the anonymity of such information can prevent the occurrence of risks. Given the paramount significance of user privacy and data integrity, this paper primarily investigates the confidentiality, integrity, and anonymity of messages. This paper aims to develop a platform for determining dynamic pricing to coordinate supply and demand, thereby maximizing the efficiency of facilities. In the previous research, the operation center was not authenticated for the customer in the first step, and they also had a heavy computational cost. But this paper has endeavored to develop an efficient and comprehensive privacy-preserving solution for the smart electricity network. Also, it has tried to cover all the required security objectives by dealing with authenticity, confidentiality, and irrefutability. The method of the research is that two entities mutually authenticate each other and reach a key agreement so that if the operation center wants to send a control command, it can send control commands directly to the meter with less time complexity. The power company sends control commands and requests to the smart meters until the analyzed and collected energy consumption data are transmitted. The data aggregator node gathers the data from the meters. The results showed that the proposed method reduced the computational complexity and communication overhead to a satisfactory level and is also resistant to various attacks. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose a conformal and flexible meander dipole rectenna array for omnidirectionally harvesting ambient RF power for application in Internet of Things (IoT) water meters. The array unit consists of an antenna for RF power harvesting and a Schottky diode for converting the harvested RF power into DC power. The impedance between the antenna and diode is directly conjugated and matched using a meander structure and coupling loop. Traditional matching networks introduce additional losses, while direct conjugate matching maximizes power transmission efficiency and reduces energy losses. The elimination of the matching network simplifies the design of the rectenna, reducing the number of components and the overall size and weight. The rectenna unit is suitable for low-power ambient energy harvesting and operates at 2.45 GHz. The measured RF to DC conversion efficiency of the rectenna unit reaches 50% at 0 dBm. The rectenna array is formed by connecting eight antenna units in parallel, and units are affixed to the four surfaces of the water meter case to achieve omnidirectional RF environmental power harvesting. The output DC power of the array can be up to 1.3 mW at 100 µw/cm2 received power density. An energy management circuit (BQ25504) is designed to store, distribute, and manage the harvesting of RF power to power the IoT water meter efficiently. Measured results demonstrated that the proposed rectenna array exhibited excellent adaptability and application potential in IoT scenarios. [ABSTRACT FROM AUTHOR]

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Internet of Things (IoT) is the amalgamation of hardware, like sensors and trackers, which monitor several parameters of the environment or physical objects, and software that processes all the data gathered by hardware. Globally, the IoT market is anticipated to reach 53.8 billion USD by 2025. This enhancing demand is due to its innate ability to automate, which drives several industries to adopt IoT. In addition, minimum memory cost, processing, and storage with an increase in Big Data (BD), cloud, and conjunction of industrial networks and the internet are the added factors for the increase in IoT development. Due to this significance, IoT has applications in numerous areas like medical management, farming, wearable technology, smart energy meters, smart cities, etc. The applications are not limited to the examples mentioned above. Considering this, existing studies have considered different applications and attempted to execute them. As different applications have been focused on by these studies, the present review intends to provide a compilation of potential applications of IoT as considered by conventional research between 2018 and 2022. The study also intends to explore the advantages and disadvantages of different IoT applications (deliberated by conventional studies) through tabular analysis. Further, this review emphasizes IoT’s major key challenges and countermeasures to resolve its security issues. Finally, the study affords recommendations that will assist all IoT experts in bringing IoT products with enhanced security into the market. [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]

Integrating the self‐healing capability realizes the automated protection of smart distribution systems. This article presents a fault location scheme for low voltage (LV) distribution systems based on the information collected from the smart meters. First, a fault condition is detected and classified by processing the current signal at the secondary side of the distribution transformer by an intelligent electronic device. Then, the faulty feeder and section are determined by calculating the fault‐imposed component of nodal voltages. The reliable performance of the proposed scheme is verified through several case studies using a real semi‐rural distribution system. [ABSTRACT FROM AUTHOR]

The new intelligent factory introduces Time-Sensitive Network into industrial Ethernet to provide real time and deterministic guarantee communication for production system. Since the problem pertaining to data leakage or damage during transmission has increasingly become pronounced, security protection technology has been introduced, but this technology will bring about a delay in user response and a decline in the quality of service. Meanwhile, ensuring the deterministic mixed transmission of time-sensitive and large-bandwidth data traffic supported by the same switching device is a still challenging problem. Therefore, this study proposes a hybrid security scheduling scheme which combines Time-Aware Shaper and cycle specified queuing and forwarding (CSQF). Specifically, the mechanism first adopts various encryption methods for different traffic, and afterward, it reduces its resource occupation by adjusting the sampling period of the time-sensitive traffic. At the same time, it adopts CSQF to schedule the large-bandwidth data traffic, thereby improving the scheduling success rate. According to the experimental results, this scheme enhances network security and network scheduling success rate by up to 51%. The scheduling of mixed traffic in the Time-sensitive Network is realized securely and efficiently. [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]

In the neighborhood area network (NAN), the advanced metering infrastructure (AMI) enables a bidirectional connection between the smart meter (SM) and the data concentrator (DC). Sensors, such as smart meter nodes or environmental sensor nodes, play a crucial role in measuring and transmitting data to central units for advanced monitoring, management, and analysis of energy consumption. Wired and wireless communication technologies are used to implement the AMI-NAN. This paper delves into a novel approach for optimizing the choice of communication medium, air for radio frequency (RF) or power lines for power line communication (PLC), between the SM and DC in the context of the AMI-NAN. The authors methodically select the specific technologies, RF and NB-PLC (narrowband power line communication), and meticulously characterize their attributes. Then, a comparative analysis spanning rural, urban, and industrial settings is conducted to evaluate the proposed method. The overall reliability performance of the AMI-NAN system requires a packet error rate (PER) lower than 10%. To this end, an efficient approach is introduced to assess and enhance the reliability of NB-PLC and RF for AMI-NAN applications. Simulation results demonstrate that wireless communication is the optimal choice for the rural scenario, especially for a signal-to-noise ratio (SNR) lower than 25 dB. However, in urban environments characterized by higher SNR values and moderately dense networks, NB-PLC gains prominence. In denser networks, it outperforms wireless communication, exhibiting a remarkable 10 dB gain for a bit error rate (BER) of 10−3. Moreover, in industrial zones characterized by intricate network topologies and non-linear loads, the power line channel emerges as the optimal choice for data transmission. [ABSTRACT FROM AUTHOR]

Considering the growing demand for electricity in industrial parks, understanding their electric power load patterns is critical for improving energy efficiency and ensuring the rational utilization of energy resources. However, the detailed electric power load data of various buildings in industrial parks are rarely available and accessible, which hinders the related studies. In this context, we present the electric power load data of 6 years (from January 1, 2016 to December 31, 2021) for various types of buildings in an industrial park in Suzhou, China. The data are obtained from smart meters and have various time resolutions (i.e., 5 minutes, 30 minutes, and 1 hour). This work describes the data collection, processing process, and different imputation methods. The high-resolution electric power load data can be used for various research tasks, including load prediction, load pattern recognition, anomaly detection, and demand response strategy development. [ABSTRACT FROM AUTHOR]

Data aggregation is considered as the crucial aspect of the smart grid to assess the electricity consumption information which acts as a basis of management decision making for utility provider. However, smart meters are lightweight devices therefore, subject to technical crashes and deferment in forwarding their data that hinders the efficient and accurate communication of data. Consequently, the consumption data of the faulty smart meters remain uncommunicated which can affect the accuracy of the final aggregate significantly and can misguide the utility companies in the power management decisions. Therefore, considering the stated challenge, a novel AI-driven fault-tolerant aggregation model is proposed that is established on the prediction mechanism of artificial neural network. The novelty of the proposed work lies in the efficient fault-handling of unavailability of data by utilizing previous available information. Furthermore, the proposed model also provides mechanism to track the affected meters and acknowledge the utility provider. The experimental simulation and comparison results reveal the outperforming results of the proposed model from state-of-the-art works which is validated using root mean squared error and mean absolute error. Additionally, the aggregation accuracy is achieved up to 99.95% with a relative improvement of 23.92%. [ABSTRACT FROM AUTHOR]

Uncertainties related to the energy produced and consumed in smart grids, especially in microgrids, are among the major issues for both their design and optimal management. In that context, it is essential to have representative probabilistic scenarios of these environmental uncertainties. The intensive development and massive installation of smart meters will help to better characterize local energy consumption and production in the following years. However, models representing these variables over large timescales are essential for microgrid design. In this paper, we explore a simple method based on Markov chains capable of generating a large number of probabilistic production or consumption profiles from available historical measurements. We show that the developed approach can capture the main characteristics and statistical variability of real data on both short-term and long-term scales. Moreover, the correlation between both production and demand is conserved in generated profiles with respect to historical measurements. [ABSTRACT FROM AUTHOR]

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μ PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μ PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μ PMU on a densely-noded distribution network. [ABSTRACT FROM AUTHOR]

With the development of infrastructure construction in mountainous areas, the number of new extra-long tunnels is increasing. However, these tunnels often face the challenge of complex and variable surrounding rock grades, resulting in a large number of overbreak and underbreak due to the untimely adjustment of smooth blasting parameters. This study focuses on the optimization of the peripheral hole charging structure and blasting parameters for extra-long hard rock tunnels, aiming to improve the effectiveness of smooth blasting technology. The results of this study demonstrate a significant improvement in the effect of smooth blasting after implementing bidirectional polymerization blasting in the tunnel. A comparison between the bidirectional shaped charge and spaced decoupled charge blasting reveals that the former yields better results. To obtain accurate data on the tunnel section profile during excavation, a laser cross-section meter is used for measurement. Furthermore, this study quantitatively compares the optimization effect of smooth blasting parameters. The multifractal characteristics of the tunnel profile overbreak point sequences are analyzed under different smooth blasting schemes using the multifractal detrended fluctuation analysis (MF-DFA) method. It is found that both the spaced decoupled charge and the bidirectional shaped charge blasting exhibit multifractal features in the overbreak measurement point sequences. The calculation results of the multifractal features of the tunnel profile under different smooth blasting plans are in line with the actual situation. [ABSTRACT FROM AUTHOR]

During the propagation of the Hinnamnor typhoon from September 5 to 6, 2022, swell waves were recorded that arrived at the recording point 20 h before the typhoon entered the Sea of Japan. While analyzing the field data of the laser meter of fluid pressure variation, it was possible to calculate the area of the formation of the main group of swell waves generated by the propagating typhoon and to localize the region of generation of forerunner waves and also to describe the mechanisms of their formation and propagation. [ABSTRACT FROM AUTHOR]

With the development of smart grid and energy Internet, more and more smart sensing devices are installed and used in the power system, thus forming the advanced metering infrastructure (AMI). It makes the power system generate massive data all the time, which may come from smart meters, digital protection devices and so on. Behavior evaluation is to filter out the selected tags that meet the type of user portrait from the user system through the known partial tags, and these similar tags together determine the user's behavior. How to make good use of the collected power big data is an important research topic in the field of power system. Using data mining technology to analyze power big data is a common research method to deal with power big data problems. To facilitate the staff to quickly understand the characteristics of the user, save service costs, improve user service satisfaction, and find the weak links of the power grid, this paper introduces the random forest algorithm, further researches on the basis of user portraits, combines database technology, machine learning algorithm and Logistics algorithm appropriately, constructs the user data sampling model based on big data, fully excavates the big data information of users and predicts the behavior of power users. According to the results of the forecast, it helps the power enterprises to correct and improve the relevant measures in the future. Experiments show that compared with the traditional Logistics model, the K-S value is increased by about 65.38%. The Gini coefficient is increased by about 12.60%, and the efficiency of the algorithm is improved by 5.26%. It has great advantages in stability and generalization. It has a better effect on power data sampling and forecasting, and helps power enterprises to improve market evaluation and corporate reputation. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid means that electric vehicles are charged when electricity is plentiful and discharged when it is scarce. New battery-electric vehicles have an energy capacity above 60 kWh installed and practically always have a DC connector. With over 1 million of such vehicles in Germany alone already, the flexibility potential to balance out fluctuating renewable generation or compensate for grid constraints is large. While many actors are working to enable this market, the readiness of hardware and regulations as well as the potential volume are hard to grasp. This paper provides an overview of these factors for Europe with a special focus on Germany. We find that some countries started to implement regulatory frameworks but none are ready yet. Issues include taxation, the fulfillment of grid codes, and the lack of smart meters. In terms of vehicles, 25 manufacturers with bidirectional charging ability were identified, but most vehicles were only used in field tests or operate in island mode. In terms of charging infrastructure, the picture is brighter with at least 20 manufacturers that offer DC bidirectional charging stations and 2 offering an AC variant. [ABSTRACT FROM AUTHOR]

Broadband powerline communication is a technology developed mainly with consumer applications and bulk data transmission in mind. Typical use cases include file download, streaming, or last-mile internet access for residential buildings. Applications gaining momentum are smart metering and grid automation, where response time requirements are relatively moderate compared to industrial (real-time) control. This work investigates to which extent G.hn technology, with existing, commercial off-the-shelf components, can be used for real-time control applications. Maximum packet rate and latency statistics are investigated for different G.hn profiles and MAC algorithms. An elevator control system serves as an example application to define the latency and throughput requirements. The results show that G.hn is a feasible technology candidate for industrial IoT-type applications if certain boundary conditions can be ensured. [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]

Power systems have been going through a barrage of transformations due to the recent developments in the field, such as deregulation and restructuring of the electric power supply chain, the proliferation of distributed generation (DG), and advancements in information and communications technologies. These have significantly impacted the approach to the planning, design, and operation of active distribution networks or systems. Due to this constant change, the system has become more complex to plan, maintain, and control. In this paper, the benefits and challenges of active distribution systems relative to traditional passive and active distribution systems are evaluated and investigated while the management and operational characteristics of demand-side resources in active distribution systems (ADS) are studied. In a typical ADS, there exist several vulnerabilities and threats that eventually pose a challenge in the control and automation of substations. These vulnerabilities and threats are reviewed, and potential mitigation measures are suggested. Also in this paper, the communication technologies and their implementation in terms of control and automation capabilities in active distribution networks are also studied. From this work, it is concluded that communication technologies play an integral role in the realization of more active distribution networks and that the Internet of Energy (IoE) is a major player in ADS in the reduction of faults due to human error, fast responses, and improving the stability of power supply. Cyber threats are also and will still be a continuous challenge in smart metering technologies and in substation automation systems (SAS), which will require frequent evaluation and mitigation measures so as not to prevent the power supply system from collapsing. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system (EMS) in smart grid based on Internet of Things (IoT) configuration using hybrid approach. The proposed approach is joint execution of Multi-fidelity meta-optimization and Turbulent Flow of water based optimization (TFWO), hence it is known as M2FWO technique. The main objective of this proposed EMS is to better manage power with the resources of Smart Grid by constantly monitoring data from the IoT-based communication framework. Here, each home device is connected to data acquisition module is utilized to facilitate the demand response (DR) growth for energy management system in smart grid. The framework collects DR from every smart home device and then transmits the data to the centralized server. The transmitting data is enabled by M2FWO method. The smart grid IoT framework enhances the feasibility of these networks makes better use of obtainable resources. The proposed system is in charge to satisfy the total supply with energy requirement. Finally, the proposed model is stimulated on MATLAB/Simulink site, then the efficiency is examined with existing methods. [ABSTRACT FROM AUTHOR]

In the current circumstances, when lakhs of people are dying as a result of the COVID‐19 Pandemic, imposing lockdown and shutdown disrupts the socioeconomic conditions of the entire globe to the point that the country's backbone is compromised. A highly modified, effective sterilizing and disinfection system is essential to improve the country or human civilization. To combat the many adverse effects of radiation, not only on human health but also on the environment, ultra violet technology has been introduced. UV technology is also a source of radiation that has health risks; however, changes in wavelength can make the risk low. UVM‐30A sensor module is used in the sanitization to detect UV irradiation in this study effort. An IoT‐based system based on Arduino uno microcontroller, GSM A6 WiFi module, and UVM‐30A is presented to create an automated implanted UV radiation detection device. [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]

The current study aimed to design and test the accuracy of a variable-rate control metering mechanism (VRCMM) and a remote smart monitoring system (RSMS) for a precision sugarcane transplanter based on IoT technology. The VRCMM is used to operate the seedling metering device at different speeds using a stepper motor based on the travel speed, and the RSMS was employed to evaluate of the three basic parameters of seedling amount, optimum rate, and missed rate. Two types of sensors were used for detecting the sugarcane seedling (SS) and travel speed, including one ultrasonic sensor and one infrared RPM sensor. The study was performed at five travel speeds and four transplant spacings. The findings of laboratory tests showed that the mean record of the relative error between the desired stepper motor speed of the VRCMM and the real value was 3.39%, and it increased with increasing the travel speed. as Additionally, the speed regulation performance was in agreement with the transplanting index. The change in RSMS accuracy is obvious when the travel speed is high and the transplant spacing is small. The RSMS accuracy drops sharply, revealing a leaping change. In conclusion, the smart and intelligent designed sugarcane transplanter would be very useful in sugarcane production. [ABSTRACT FROM AUTHOR]

Smart Grids (SG) encompass the utilization of large-scale data, advanced communication infrastructure, and enhanced efficiency in the management of electricity demand, distribution, and productivity through the application of machine learning techniques. The utilization of machine learning facilitates the creation and implementation of proactive and automated decision-making methods for smart grids. In this paper, we provide an experimental study to understand the power demands of consumers (domestic and commercial) in SGs. The power demand source is considered a smart plug reading dataset. This dataset is large dataset and consists of more than 850 user plug readings. From the dataset, we have extracted two different user data. Additionally, their hourly, daily, weekly, and monthly power demand is analysed individually. Next, these power demand patterns are utilized as a time series problem and the data is transformed into 5 neighbour problems to predict the next hour, day, week, and month power demand. To learn from the transformed data, Artificial Neural Network (ANN) and Linear Regression (LR) ML algorithms are used. According to the conducted experiments, we found that ANN provides more accurate prediction than LR Additionally, we observe that the prediction of hourly demand is more accurate than the prediction of daily, weekly, and monthly demand. Additionally, the prediction of each kind of pattern needs an individually refined model for performing with better accuracy. [ABSTRACT FROM AUTHOR]

Abstract Electricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers’ electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors’ model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively.

The integration of Smart Grid technology and conceptual Industry 5.0 has paved the way for advanced energy management systems that enhance efficiency and revolutionized the parallel integration of power sources in a sustainable manner. However, this digitization has opened a new stream of the threat and opportunities of electricity theft posing a significant challenge to the security and reliability of Smart Grid networks. In this paper, we propose a secure and reliable theft detection technique using deep federated learning (FL) mechanism. The technique leverages the collaborative power of FL to train a Convolutional Gated Recurrent Unit (ConvGRU) model on distributed data sources without compromising data privacy. The training deep learning model backbone consists of a ConvGRU model that combines convolutional and gated recurrent units to capture spatial and temporal patterns in electricity consumption data. An improvised preprocessing mechanism and hyperparameter tuning is done to facilitate FL mechanism. The halving randomized search algorithm is used for hyperparameters tuning of the ConvGRU model. The impact of hyperparameters involved in the ConvGRU model such as number of layers, filters, kernel size, activation function, pooling, GRU layers, hidden state dimension, learning rate, and the dropout rate is elaborated. The proposed technique achieves promising results, with high accuracy, precision, recall, and F1 score, demonstrating its efficacy in detecting electricity theft in Smart Grid networks. Comparative analysis with existing techniques reveal the superior performance of the deep FL-based ConvGRU model. The findings highlight the potential of this approach in enhancing the security and efficiency of Smart Grid systems while preserving data privacy.

— Reading traditional meters is always time-consuming and expensive. Using smart meters solves most of the problems existing in the traditional meter network. Smart meters are an advanced form of traditional electro-mechanical devices that can measure energy consumption in real-time and communicate through one or more wired or wireless networks. These devices can communicate from long distances and get changed, making them an easy target for attacks. This paper studies the security mechanisms in smart meters networks and suggests some security solutions in such networks. We have developed software for managing the information of smart meters and controlling them remotely. In this paper, we present the implemented security mechanisms in the developed smart meter management software. The proposed solutions for enhancing the security of this software include implementing the authentication system, enabling user management, and defining different access levels to prevent users from connecting without proper authentication and access control in the developed software. Moreover, hashing the password with a random salt technique is implemented for securing the database. Furthermore, we have secured the software platform to prevent web attacks such as Clickjacking and CSRF attacks.