Search Results (615)

Smart energy meters are widely deployed in modern distribution networks, extending their role beyond revenue billing to real-time monitoring and data-driven smart city applications. However, conventional legal metrology frameworks rely on periodic recalibration and are not intended for the detection of accuracy drift or unexpected malfunctions between scheduled inspections. In scientific publications, various techniques for remote smart meters’ error surveillance are presented, but experimental verification on real distribution network data remains limited. The objective of this study is to experimentally verify two previously proposed power event-driven methods for remote estimation of active power measurement error in individual consumer meters, using a feeder-level sum meter as a reference instrument. One-second resolution electrical readings were collected from a real low-voltage distribution branch using ESP32-based local adapters communicating via MQTT over Wi-Fi, with SNTP-based clock synchronization for power event correlation. Under optimized detection parameters, the linear regression method achieved 0.20% RMSE and 0.75% maximum absolute error, and the neural network method 0.09% RMSE and 0.31%, confirming suitability for Class 1 m accuracy surveillance. Feasibility analysis of three MQTT-based deployment scenarios demonstrates that binary encoding limits local adapter buffers to 2.8 kB and worst-case daily channel demand to 2000 kB, confirming the practical viability of the proposed architecture. Full article

Demand response (DR) plays a key role in enhancing power system flexibility under increasing renewable penetration, yet most existing approaches rely on aggregate demand models that fail to capture appliance-level heterogeneity. A bilevel programming framework for DR incentive design incorporating non-intrusive load monitoring (NILM)-based flexibility estimation is proposed. A conditional factorial hidden Markov model (CFHMM) is used to disaggregate smart meter data and recover appliance-level consumption patterns, which are then mapped to willingness-to-accept (WTA) values to construct device-informed DR potential functions. These estimates are embedded in a bilevel optimization model, where a retailer determines optimal incentives while accounting for the endogenous impact of demand response on locational marginal prices through market clearing. The model is reformulated as a single-level mixed-integer linear program using Karush–Kuhn–Tucker (KKT) conditions. Case studies using real-world data and the IEEE test system show that the proposed framework produces more effective incentive strategies than aggregate DR modeling, leading to improved DR utilization and higher retailer profitability. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

Global environmental challenges—ranging from climate change to resource depletion—require not only technological innovation but also sustained shifts in household behavior. Two principal policy tools have emerged to promote such shifts in residential communities: financial incentives (e.g., subsidies, rebates, dynamic pricing) and educational campaigns (e.g., information provision, social norms messaging, feedback systems); yet rigorous comparative evidence on their relative intervention effectiveness —defined here as the magnitude of behavioral change achieved—remains fragmented. The aim of this review is to systematically compare the effectiveness of financial incentives and educational campaigns for promoting pro-environmental behaviors in residential communities, and to identify the conditions under which each approach performs best. This systematic review addresses: How do financial incentives compare to educational campaigns in promoting pro-environmental behaviors in residential communities? Through PRISMA 2020 methodology, synthesizing 51 studies including 5 major meta-analyses (2015–2024), comparative intervention effectiveness evidence is provided. Financial incentives achieve modest reductions (1.8–6.0%, g = 0.36) with rapid adoption but substantial rebound effects (35–60% offset) and poor persistence post-removal. Educational campaigns show higher variability (g = 0.23 to 0.93), with targeted approaches achieving up to 8% reductions, better persistence (57% effect retention at 24 months), and lower rebounds (15–30%). Combined approaches demonstrate the largest effects (g = 0.64) and optimal cost-effectiveness. Context determines effectiveness: financial incentives excel for high-cost technology adoption; and educational campaigns for habitual behaviors. Technology-mediated delivery (smart meters, mobile apps) enhances both approaches. The principal contribution of this review is a comprehensive umbrella synthesis to directly compare both intervention paradigms while simultaneously accounting for rebound effects, moral licensing, age-specific moderators, and cost-effectiveness, offering practitioners an integrated evidence base for intervention selection. We conclude with evidence-based recommendations for intervention selection. Full article

Low-voltage distribution networks are critical for supplying power to end-users, and electric shock safety is a key concern; however, the frequent incompleteness of topology information in practical operations makes it challenging to accurately assess electric shock risks. This paper proposes a two-stage approach for electric shock simulation and risk assessment in low-voltage distribution networks with completely unknown topology and absent phase-angle measurements, addressing the critical challenge of unavailable, incomplete, or outdated topology information using only conventional smart meter data. It innovatively investigates shock risks under TT, TN-C, and TN-S grounding systems without prior topology knowledge or synchronized phasors. The proposed methodology combines a phase-angle-agnostic data-driven stage and a model-driven stage: the data-driven stage uses an iterative algorithm for topology label matrix estimation and weighted Laplacian matrix reconstruction with hierarchical clustering to identify network structure and line parameters, requiring only active power, reactive power, voltage magnitude, and current magnitude. The model-driven stage adopts modified nodal analysis with the finite-difference time-domain (MNA-FDTD) method to evaluate transient leakage voltage distribution under single-phase-to-ground faults, thereby assessing electric shock risks in line with international safety standards. Key contributions include a practical phase-free topology identification framework, comparative risk analysis of three grounding systems, and an integrated data-model approach for real-world low-observability networks. Simulation results show accurate topology/parameter identification with a relative Frobenius-norm error of only 1.8% even without phase data. TN-S provides the highest safety complying with IEC standards, followed by TN-C and TT under specific conditions, offering a practical solution for utilities lacking detailed topology records. Full article

Electricity theft remains a critical challenge that destabilizes power systems, causes significant financial losses, and disrupts the grid, particularly in developing countries. This study presents a machine learning framework integrating an ANN and advanced performance metrics to accurately detect fraud consumption patterns in a smart meter dataset. The method achieves strong categorization between normal and abnormal conduct by simulating temporal behavior across seasons, applying feature extraction to high-resolution energy signals, and assessing performance using RMSE, MAE, and R². The experimental results demonstrate that intelligent algorithms significantly improve theft-detection accuracy; reduce losses, especially NTLs; and provide a scalable foundation for future smart-grid security. Full article

The existing studies on Small-Scale Embedded Generation (SSEG) have not addressed the net billing framework behavior that applies to different import and export tariff rates. This paper presents the simulation and modeling of a smart net billing electricity meter for SSEG in MATLAB/Simulink R2018b. The model integrates a PV array, MPPT controller, DC-DC boost converter, three-phase voltage source inverter (VSI), LC filter, synchronous generator, and a bidirectional energy meter. A smart billing subsystem was developed to compute real-time energy costs using differential tariff rates consistent with South African utility policies. Simulations were conducted under fixed irradiance, with electrical performance evaluated over a short interval and billing dynamics assessed over an extended period. Results show stable PV generation, proper inverter synchronization with the utility grid, and accurate tracking of imported and exported energy. The system effectively calculates the net bill, demonstrating transparency, automation, and economic accuracy in line with policy-driven net billing frameworks. These outcomes validate the technical feasibility and practical relevance of smart net billing meters in modern grid-connected renewable energy applications. Full article

Smart water grid technologies have been widely adopted as a key component of digital transformation in water resource management, where real-time household water consumption data collected from smart water meters serve as fundamental inputs. However, these datasets often contain numerous outliers and missing values due to communication errors, which degrade data reliability and hinder accurate analysis. This study proposes an improved framework for outlier detection and missing data imputation tailored to the characteristics of cumulative household water consumption data. The proposed imputation methods were evaluated against conventional approaches using error metrics, and the results demonstrated significant improvements in accuracy, with RMSE values substantially lower than those of the reference method. In addition, prediction models with varying levels of complexity were explored to examine how improved data quality influences forecasting performance. The results indicate that, although data preprocessing enhances data reliability, prediction performance remains limited due to the inherent variability and stochastic nature of household water consumption data. Prediction models with varying levels of complexity were constructed and evaluated using the corrected datasets. The performance of the models varied depending on dataset characteristics, and no single model consistently outperformed others. Overall, this study highlights the critical role of data quality improvement in smart water management systems and provides practical insights into missing data imputation, while suggesting that further advancements in prediction require additional explanatory variables and more sophisticated modeling approaches. Full article

Leakage localization in water distribution networks (WDN) is crucial for reducing water losses, conserving energy, and improving system efficiency. This work presents the application of an integrated approach for leakage localization, relying on sub-daily pressure and inflow data, water-consumption data obtained by means of smart meters, and the WDN digital twin. Observed and simulated pressure data are iteratively compared across multiple leakage scenarios to identify the spatial distribution that minimizes discrepancies, thus pinpointing likely leakage areas. The approach was tested on a real-world WDN where about one-third of users are equipped with smart meters. Results demonstrated its effectiveness in accurately localizing leakages and supporting prompt, targeted repairs. Full article

The importance of considering the photovoltaic (PV) output in distribution system operations and planning has increased. Voltage violations and equipment overloads may occur during PV output peaks, making accurate power flow analysis under such conditions essential. However, the PV output is typically measured as 30 min aggregated values by smart meters, which may underestimate the peak output and related power flow fluctuations. Installing high-resolution sensors at all the PV sites can address this issue; however, the associated costs are high. As a cost-effective alternative, high-resolution sensors can be deployed at representative PV sites, and their measurements can be used to estimate short-term outputs at surrounding PV sites. Implementing such an approach requires a quantitative evaluation of the relationship between the sensor number and PV output estimation accuracy. In the Chubu area of Japan, a trial region with sufficient high-resolution PV sensors exists, enabling detailed evaluation. This study developed a framework to estimate short-term PV outputs from representative sensors and used field data from the demonstration area to quantitatively assess the relationship between sensor deployment and estimation accuracy. These results provide guidance for designing cost-effective sensor placement strategies for practical network operations. Full article

The rapid increase in residential electricity consumption in the Philippines has underscored the need for accurate metering, predictive forecasting, and improved protection against electrical hazards. We design a residential smart energy metering system with load forecasting using Long Short-Term Memory (LSTM) and integrated overload protection. The system employs non-invasive SCT-013 current sensors manufactured by DFRobot and a ZMPT101B voltage sensor manufactured by Qingxian Zeming Langxi Electronic, both the SCT-013 and ZMPT101B were purchased from Circuit Rocks Philippines. The SCT-013 current sensors and ZMPT101B voltage sensors are interfaced with a Raspberry Pi to measure consumption across four residential branch circuits. Data is transmitted to a cloud-based dashboard for real-time monitoring, while a relay module automatically disconnects loads under overload conditions. To validate accuracy, the prototype was deployed for 24 h and recorded a total of 18.87 kWh compared to the 20 kWh recorded from the actual Manila Electric Company (MERALCO)-provided energy meter. The LSTM model trained on per-minute data with calendar features achieved strong predictive performance across the branches. The LSTM forecasted the load and current for the next 24 h. The forecasted current was used as the dynamic tripping value for the overload protection. The overload protection tests demonstrated reliable tripping behavior within seconds of detecting overload currents. Results confirm that the system provides accurate energy monitoring, reliable overload protection, and robust short-term load forecasting. The prototype demonstrates a cost-effective and scalable approach for enhancing residential energy management, safety, and forecasting in Philippine Households. Full article

Electricity theft is a critical source of non-technical losses in modern power systems, causing substantial financial and operational challenges for utilities. Traditional detection methods, such as manual inspections, are inadequate to detect advanced theft techniques, including meter tampering and cyberattacks on smart grids. This study introduces a machine learning-based framework for electricity theft detection using the TDD2022 dataset (derived from OEDI) and evaluates multiple algorithms—Random Forest, Decision Tree, XGBoost, LightGBM, CatBoost, Extra Trees, and Logistic Regression. To address class imbalance, SMOTE is applied, while feature selection leverages LASSO and ReliefF. Experiments compare electricity-only data with multi-utility inputs (electricity and gas) under balanced and imbalanced conditions. Results show that tree-based ensembles, particularly Extra Trees combined with SMOTE and ReliefF, achieve superior performance (accuracy $>95\%$, AUC $\approx 0.99$). Consumer-specific models outperform global models, with commercial classes yielding near-perfect detection, while residential profiles remain challenging. The findings highlight the importance of tailored modeling and feature selection for scalable, accurate theft detection in smart grid environments. Full article

The rapid development of smart energy infrastructures and renewable energy systems requires advanced sensing solutions that provide high accuracy, expandability, and stability under real operating conditions. However, conventional gas monitoring systems are predominantly based on resistive or voltage-output sensors, which require complex analog front-end circuits and analog-to-digital conversion, leading to increased system complexity, cost, and susceptibility to electromagnetic interference. This paper tackles this limitation by proposing a frequency-domain sensing approach for multichannel monitoring of biogas plant parameters. The objective of this study is to develop and experimentally validate an extendable sensing architecture based on autogenerator microelectronic gas transducers with direct gas concentration–frequency conversion and FPGA-based digital acquisition. The proposed method is grounded in a physical–mathematical model of the space-charge capacitance of gas-sensitive semiconductor structures derived from Poisson’s equation, facilitating analytical formulation of conversion and sensitivity functions. A multichannel FPGA-based measurement system is implemented to process frequency signals without analog conditioning or ADC stages. Experimental validation was performed for CH₄ (0–85%), CO₂ (0–60%), H₂, NH₃, and H₂S (1–20,000 ppm). The results demonstrate measurement uncertainty within 0.25–0.5%, with sensitivity reaching 350–748 Hz/ppm for H₂, 455–750 Hz/ppm for NH₃, and 253–375 Hz/ppm for H₂S, while methane and carbon dioxide sensitivities reach up to 112 kHz/% and 98.7 kHz/%, respectively. Spectral analysis in the LTE-1800 band confirms improved noise immunity (up to 4.5×) and extended transmission capabilities. A 12-channel FPGA-based monitoring system (RDM-BP-1) with a 1 s sampling interval, IP67 protection, and wireless connectivity is developed and validated. The proposed architecture eliminates analog signal conditioning, reduces hardware complexity, and provides an easily expandable and reliable sensing solution for smart buildings, renewable energy systems, and cloud-integrated energy infrastructures. Full article

Integrating existing tree-level information into harvester operator decision-making can significantly enhance precision forest management, particularly with respect to biodiversity preservation and climate-smart adaptation. During harvester operations, a primary challenge lies in positioning the machine with sufficient accuracy in real time to relate a priori individual-tree-level reference information to the operator. We propose a lightweight procedure using tree-to-tree matching to continuously register a real-time tree map collected from a harvester (or another mobile laser scanning system) to a precomputed reference map derived from an airborne laser scanner (ALS). We assess the robustness of the method using simulated tree maps and validate its real-world performance in experiments using a LiDAR-equipped harvester performing a thinning operation in a boreal forest. In simulations, registration was found to be robust up to a moderate tree density of approximately 1700 ha⁻¹, even when using a reference map with a lower positional accuracy and higher error rates than in our harvester experiments. Using real-world data from the thinning operation, the registration method was demonstrated to successfully mitigate meter-scale positioning drifts remaining in the LiDAR-inertial trajectory. After the continuous registration procedure, the positioning error was reduced to the level of 0.5 m, constrained by the accuracy of the prior map derived from sparse ALS data with ∼5 transmissions/m². Importantly, the registration procedure was shown to update in real time (at most 20 ms update time for stands with densities of at most 2000 ha⁻¹, after an initial computational phase. Notable features of the registration procedure are its low memory consumption, fast runtime and capacity to accurately position the harvester despite LiDAR-inertial positioning drift. While these results demonstrate the potential for real-time operation, full implementation requires the development of real-time tree detection and estimation of tree attributes. Full article

Background: Efficient parking navigation in large and dynamic parking areas requires systems that can adapt to real-time conditions and provide precise vehicle localization. Methods: This paper presents a smart car parking navigation module that integrates camera-based vehicle perception, homography-based ground-plane localization, mobile GNSS positioning, and dynamic route planning into a unified framework. Instance segmentation (YOLOv8n-seg) is used to detect vehicles and extract ground-contact regions, which are associated with parking slots defined in a GeoJSON-based site model. Mobile GNSS data are fused with visual observations via spatio-temporal proximity scoring to enable robust user–vehicle matching without optical identification. An A* routing algorithm dynamically computes and updates navigation paths, adapting to lane obstructions and slot availability in real time. Results: Experimental evaluation on a real six-camera parking facility shows that the proposed segmentation-based localization reduces mean error from 0.732 m to 0.283 m (61.3% improvement), with the 95th-percentile error dropping from 1.892 m to 0.908 m, and outperforming the bounding-box baseline in 85.3% of detections. Conclusions: These results demonstrate that sub-meter vehicle localization and reliable user–vehicle association are achievable using standard surveillance cameras without specialized infrastructure, offering a scalable and cost-effective solution for intelligent parking navigation. Full article