Explore the vast range of titles available.

Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.

In this paper, an introduction and comprehensive analysis have been presented for the implementation and application of modern smart meters which include Unbundled Smart Meters (USM) and Next-Generation Open Real-Time Smart Meters (NORM). This article also contributes to methods through which USM and NORM could provide a better perspective to the already available technologies for grid-tied-inverter controlled feeding renewables to the grid. The research proposes a next-generation smart meter model with the feature of a phasor measurement unit. The meter is further integrated with a controller board that controls the power injection from the inverter to the grid based on the real-time data obtained from the smart meter. The inverter is simulated with an open-circuit fault and is controlled to provide non-oscillatory power to the grid based on an instantaneous grid power factor or phase requirement. The proposed meter has the flexibility to add additional features to control the inverter based on other grid requirements such as active and reactive power control, tariff implementation, etc. This manuscript provides the analytical aspects of the use of smart meters in efficient energy management and also addresses the need for smart technologies for grid modernization.

Real-time monitoring, data-driven decisions, and energy consumption optimization have reached a new level with IoT advancement. However, a significant challenge faced by intelligent nodes and IoT applications resides in their energy requirements in the long term, especially in the case of gas or water smart meters. This article proposes an algorithm for smart meters’ energy consumption optimization based on IoT, LoRaWAN, and NB-IoT using microcontroller-based development boards, PZEM004T energy meters, Dragino LoRaWAN shield, or BG96 NB-IoT modules. The algorithm adjusts the transmission time based on the change in data in real-time. According to the experimental results, the energy consumption and the number of packets transmitted significantly decreased using LoRaWAN or NB-IoT, saving up to 76.11% and 86.81% of the transmitted packets, respectively. Additionally, the outcome highlights a notable percentage reduction in the energy consumption spike frequency, with NB-IoT achieving an 87.3% reduction and LoRaWAN slightly higher at 88.5%. This study shows that adaptive algorithms are very effective in extending the lifetime of IoT nodes, thereby providing a solid baseline for scalable, lightweight, energy-monitoring IoT applications. The results could help shape the development of smart energy metering systems and sustainable IoT.

This paper develops a novel approach for distribution system monitoring and state estimation, where time synchronisation of smart‐meter measurements is carried out via the Precision Time Protocol (PTP). The approach is based on the concept of a Modified Smart Meter (MSM), a distribution system monitoring instrument that enables accurate time synchronisation of smart meter data. The design, application, communication technique and protocols of the MSM are described in detail. The proposed MSM device features PTP‐based time synchronisation of smart meter measurements, and the concept of unbundling is applied to collect measurements utilising the existing smart meter sensors. This is expected to reduce the overall implementation cost of an MSM‐based distribution network monitoring system compared to a system based on Phasor Measurement Units (PMUs). The problem of requiring open sky access for GPS links can potentially be solved by means of PTP synchronisation. Three‐phase state estimation simulations using the IEEE‐13 and 123 bus unbalanced test networks are employed to demonstrate the applicability of the MSM, and its performance is compared to standard PMU devices. The results indicate that the MSM may represent a workable monitoring solution for MV and LV distribution networks, with an acceptable trade‐off between cost and performance.

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

Most available smart meters sample at low rates and transmit the acquired measurements to a cloud server for further processing. This article presents a prototype smart meter operating at a high sampling frequency (15 kHz) and performing energy disaggregation locally, thus negating the need to transmit the acquired high-frequency measurements. The prototype’s architecture comprises a custom signal conditioning circuit and an embedded board that performs energy disaggregation using a deep learning model. The influence of the sampling frequency on the model’s accuracy and the edge device power consumption, throughput, and latency across different hardware platforms is evaluated. The architecture embeds NILM inference into the meter hardware while maintaining a compact and energy-efficient design. The presented smart meter is benchmarked across six embedded platforms, evaluating model accuracy, latency, power usage, and throughput. Furthermore, three novel hardware-aware performance metrics are introduced to quantify NILM efficiency per unit cost, throughput, and energy, offering a reproducible framework for future NILM-enabled edge meter designs.

Identifying the topology of low-voltage (LV) networks is becoming increasingly important. Having precise and accurate topology information is crucial for future network operations and network modelling. Topology identification approaches based on smart-meter data typically rely on Root Mean Square (RMS) voltage, current, and power measurements, which are limited in accuracy due to factors such as time resolution, measurement intervals, and instrument errors. This paper presents a novel methodology for identifying distribution network topologies through the utilisation of smart-meter harmonic data. The methodology introduces, for the first time, the application of voltage Total Harmonic Distortion (THD) and individual harmonic components (V2–V20) as topology identifiers. The proposed approach leverages the unique properties of harmonic distortion to improve the accuracy of topology identification. This paper first analyses the influential factors affecting topology identification, establishing that harmonic distortion propagation patterns offer superior discrimination compared to RMS voltage. Through systematic investigation, the findings demonstrate the potential of harmonic-based analysis as a more effective alternative for topology identification in modern power distribution systems.

Although technologies spurred by the “Internet of things” are increasingly being introduced in homes, only a few studies have examined the adoption or diffusion of such household technologies. One particular area of interest in this context is electricity consumption, especially the introduction of smart metering technology (SMT) in households. Despite its growing prominence, SMT implementation has met with various challenges across the world, including limited adoption by consumers. Thus, this study empirically examines the antecedents of SMT adoption by potential consumers. Using a mixed-methods design, the study first unearths the SMT-specific antecedents, then develops a contextualized model by drawing on theories from motivational psychology and the antecedents identified earlier, and finally tests this model using a large-scale survey of German consumers. The results provide support for many of the hypotheses and highlight the importance of motivational factors and some household demographic, privacy, and innovation-related factors on consumers’ intention to adopt SMT.

This study examines the relationship between mobile app usage and residential electricity consumption. We focus on how smart meter feedback influences energy-saving behaviors under a progressive tariff system in South Korea. The study uses a combination of three datasets—daily electricity consumption, mobile app access logs, and demographic survey data—gathered from 284 households. A panel vector autoregression (VAR) model and a difference-in-difference (DID) approach are used to analyze the dynamic relationship between app engagement and energy use. The results show that daily app access does not significantly affect electricity consumption, on average. However, under a progressive tariff system, households nearing a tariff stage threshold demonstrate a reduction in electricity use when engaging with the app. This effect is strongest among households with smaller living areas, smaller household size, and no children. This study is among the first to provide empirical evidence on the impact of smart metering mobile apps in a real-world setting. Our findings underscore the importance of tailored feedback strategies to maximize energy efficiency through smart meter technology.

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.