Search Results (459)

Non-intrusive load monitoring (NILM) provides a cost-effective way to obtain appliance-level electricity information from aggregate smart-meter measurements and is therefore important for energy management, demand-side response, and sustainable operation in smart buildings. However, accurate appliance-level power disaggregation remains challenging because residential load signals usually involve overlapping appliance signatures, sparse activations, heterogeneous temporal patterns, and transient switching events. To address these challenges, this paper proposes a State-Event-Guided Multi-Scale Gated Network (SEMG-Net) for NILM. The proposed framework integrates a residual temporal encoder, multi-scale dilated convolutional blocks, and a state-event-guided gating mechanism within a unified multi-task learning architecture. The shared encoder extracts hierarchical temporal representations from aggregate mains windows, while task-specific branches jointly estimate appliance power, on/off state, and switching event type. The predicted state probability, three-class event probability distribution, and shared temporal representation are jointly used to construct a continuous gate that modulates the raw power estimate, thereby directly incorporating behavioral predictions into final power estimation. Experimental results on public datasets show that SEMG-Net achieves competitive overall performance, with clear advantages in power estimation, energy consistency, and state identification, particularly for appliances with complex operating stages or transient switching behavior. The ablation results further demonstrate the benefits of multi-scale feature extraction and auxiliary supervision, as well as the effectiveness of the proposed state-event-guided power modulation mechanism. Full article

Aggregate building indicators can obscure substantial differences in electrical demand and thermal operation among monitored spaces. This case study examined 89 days of one-minute building management system (BMS) and smart-meter records from a multi-zone university building. Routine data-quality screening was combined with physical interpretation and task requirements to determine how the available channels could be used in the exploratory analyses. The resulting map specifies each channel’s permitted use and any associated monitoring priority. Meter TR1 was selected as the primary monitored-load proxy, whereas Meter G was retained for comparative diagnosis. Primary energy analysis used 59 fully observed TR1 days without imputation or scaling. Sensitivity analyses used all 61 days meeting the 90% and 95% completeness thresholds and reported their energy values as available-minute energy aggregates. Across the 59 fully observed days, weekday energy exceeded weekend energy, and complete 24 h TR1 energy was strongly inversely associated with daily mean outdoor temperature (Spearman ${\rho }_{\mathrm{s}}=-0.889$). Of the 21 monitored spaces, five were warm-biased, four were cold-biased, and 12 were near setpoint. Within the observed winter-to-shoulder-season period, the data supported exploratory load and zone-temperature modeling. The combined assessment established record eligibility and channel use in this incomplete real-world export, and it identified priorities for later monitoring. Full article

The rapid proliferation of Internet of Things (IoT) technologies and smart grids has substantially intensified the cybersecurity challenges associated with smart energy meters (SEMs). The data collected by the plugs are transmitted via a wireless communication protocol to a smart electricity meter that acts as a local gateway. This meter centralizes the information from the various sensors, may perform data pre-processing, aggregation, or validation operations, and then forwards the information to a central server. The main contributions of this system can be categorized into two key aspects. First, the implementation of a centralized wireless local energy consumption network using the Wi-Fi protocol to coordinate smart plugs over distances of up to 20 m. Second, the real-time acquisition of power characteristics and the remote control (ON/OFF switching) of household appliances for direct appliance-level submetering purposes. Data collected by the smart meter are transmitted to a processing unit through a Semtech SX1276 LoRa transceiver communication link. The central server constitutes the processing and storage layer of the system: it receives the collected data, archives it in a dedicated database, and makes it available through analysis, visualization, and decision-support tools. This architecture enables real-time monitoring of energy consumption, anomaly detection, optimization of electrical resource use, and the development of effective energy management strategies for smart electrical grids. Although current smart meter architectures incorporate multi-layer protection mechanisms at the hardware, communication, and data levels, additional security measures are required to counter advanced cyber threats aimed at data interception and manipulation. This paper improves the security framework of smart energy meters by integrating a chaos-based encryption layer to ensure secure data transmission. Chaotic systems exhibit intrinsic properties such as sensitivity to initial conditions, pseudo-randomness, and ergodicity, which render them particularly suitable for cryptographic applications. The proposed framework employs a Lorenz-based chaotic encryption module to secure SEM-utility data exchanges. Full article

Public electric vehicle (EV) charging increasingly relies on internet-connected platforms for metering, billing, roaming, and settlement. However, final billing records and charge detail records often provide limited evidence that a session result can be independently linked to the ordered metering data from which it was derived. This paper presents a reproducible blockchain-anchored Proof-of-Charge platform for generating tamper-evident and auditable EV charging receipts. The platform converts charging-session data into canonical receipts, computes cryptographic commitments over receipt content and ordered meter values, aggregates receipt hashes using a temporally ordered and domain-separated Merkle profile, and anchors compact batch commitments in a smart contract while keeping detailed records off-chain. A working prototype implements cross-language canonicalization checks, membership-proof generation, structured storage, batch anchoring, verification services, synthetic workload generation, dataset export, and local blockchain deployment. Across 50 measured runs covering 10 to 1000 receipts, all count reconciliations, batch-root checks, and on-chain comparisons passed. Mean receipt-pipeline latency ranged from 7.152 to 7.853 ms per receipt, with throughput from 127.54 to 141.23 receipts/s. A focused 1000-leaf proof sample produced a 2064-byte proof with ten sibling hashes. The results show that the platform can generate, anchor, and verify auditable EV charging receipts with reproducible performance while keeping detailed charging data off-chain. The proposed architecture provides a practical digital trust layer for internet-enabled EV charging and V2G-ready settlement workflows. Full article

With the large-scale deployment of smart electricity meters in China, power companies need online methods that can evaluate meter operating status and locate faulty units without field inspection. Meter data are inherently multimodal. They combine time-series measurements with textual event logs. The semantic gap between these modalities limits the accuracy of existing approaches. This paper proposes an online operational status evaluation method for smart meters based on cross-modal perception with large language models. A Bi-LSTM and TCN-Attention network with quantile regression first builds a robust district line-loss baseline that captures seasonal fluctuations and operational uncertainty. A cross-modal alignment module then fuses the two modalities. The measurement sequences are encoded by PatchTST, and the event logs are encoded by a LoRA-fine-tuned LLM. The fusion is performed through contrastive learning and gated fusion. A retrieval-augmented knowledge graph provides additional support. Finally, a multi-indicator health index grades meters into five condition levels, and a hidden Markov model estimates the remaining useful life. Case study results demonstrate that the proposed method achieves 85.7% accuracy, outperforming the strongest baseline. Full article

Low-voltage violations in rural distribution transformer areas (RDTAs) are a persistent and widespread problem in China, affecting over 5% of such areas and compromising the power supply quality for millions of rural end-users. These violations are primarily caused by long supply distances, undersized conductors, dispersed load distributions, and low power factors at terminal feeders, resulting in degraded equipment performance, increased line losses, and hindered integration of renewable distributed generation. The key to mitigating such violations lies in the rapid and coordinated regulation of primary equipment. Considering the actual equipment configurations in rural distribution transformer areas, a low-voltage mitigation method based on power-mileage aggregation is proposed. First, a generative adversarial network is employed to preprocess the collected electrical data, and device ranging is achieved through power-line characteristic pulses, enabling the spatiotemporal distribution analysis of low-voltage phenomena. Second, based on the line lengths from power sources—including distributed generators, soft open points, energy storage systems, and distribution transformers—to the smart meters, a power-mileage aggregation metric is developed to quantify the impact of power-source output variations on low-voltage meters. Subsequently, with the objective of minimizing the power mileage for low-voltage mitigation, the Newton iteration method is applied to determine the optimal output powers of energy storage systems and soft open points. The effectiveness of the proposed method is validated through case studies. Comparative analyses with optimal power flow, second-order cone programming, and interval robust control demonstrate that the proposed approach achieves superior low-voltage mitigation performance while maintaining higher economic efficiency. Full article

Non-OCPP electric vehicle (EV) chargers lack site-integrated monitoring and supply-control, limiting their use in real-time energy management systems. This paper presents a Smart Distribution Panel that closes this gap via panel-side observability and an operator-commanded single-phase/three-phase (1$\mathsf{\Phi }$/3$\mathsf{\Phi }$) supply reconfiguration. In a single-site feasibility study, the panel is characterised with one consumer Tesla Wall Connector Gen 3 (Non-OCPP) and one Tesla Model Y at a Taiwan 3$\mathsf{\Phi }$3W 220 V site, using a CPM-80 meter (IEC 62053-22 class 0.2S), interlocked contactors, an e-stop relay, and a Raspberry Pi 4 data path. Phase-mode transitions interrupt charging for ∼5 s, resolved by the IEC 61851-1 handshake. A 74.5-min stepwise session—driven by the OCPP DC fast charger with the Non-OCPP Wall Connector idle—yields a site-specific ensemble PCC-bus sensitivity $V=224.85-0.147P$ ($R^2=0.991$), characterising the site + DC-charger + base-load ensemble observed through the panel’s metering rather than the panel’s Non-OCPP path; an OpenDSS bounded sanity check gives an upper-bound slope of 0.26–0.28 V/kW. The one-second data stream supports a non-autoregressive (Non-AR) LSTM residual-detection layer (8-seed RMSE $0.310\pm 0.084$ V). A synthetic voltage-drop sensitivity sweep (ROC-AUC 0.81–0.97) characterises sensitivity to injected perturbations, not real-world fault detection. Feasibility is demonstrated only for the tested single-site configuration; multi-site, multi-EVSE, multi-EV generalisation, autonomous demand response, and OCPP session-level features are not demonstrated and are stated as future work. Full article

The construction of high-fidelity virtual factories relies heavily on the accurate reconstruction of historical production timelines. While traditional Manufacturing Execution Systems (MESs) provide idealized, static schedules, they inherently struggle to capture the “stochastic variability” and “undefined events” caused by machine-specific behaviors on the industrial shop floor. To bridge the gap between top-down scheduling and bottom-up physical reality, this study proposes the Power-Aware State Segmentation for Solder Paste Printers (PAS-SPP) algorithm. Utilizing non-intrusive, high-frequency continuous power features captured via an Industrial Internet of Things (IIoT) architecture with PA310 meters, the algorithm employs a synergistic combination of amplitude thresholding (${\theta }_{high}$) and temporal constraints (${\tau }_{blank}$, ${\tau }_{min}$) to actively filter transient electrical noise and accurately bound macroscopic operational blocks. This robust filtering thereby avoids the accuracy degradation commonly caused by noise interference in the analysis processes of traditional machine learning models. Consequently, the mechanism effectively decouples operational states into a virtual Solder Paste Printer (vSPP) behavioral meta-model integrated with a Finite State Machine (FSM). Empirical validation across distinct production cases demonstrates that the proposed model not only accurately extracts standard 33–35 s cycle times but also reveals critical hidden characteristics, such as 68 s automated cleanings and dynamically adjusted “3-to-1” print-to-clean ratios. Furthermore, a comprehensive comparative analysis was conducted against static MES logs, Naive Power Thresholding (NPT), and a Gaussian Hidden Markov Model (GMM-HMM). Evaluated under identical manufacturing-process conditions, the results reveal that PAS-SPP effectively mitigates the cascading misalignments in static schedules and avoids the severe over-segmentation limitations inherent in point-by-point probabilistic decoding, thereby achieving highly accurate state decoupling. Finally, this study systematically defines the method’s applicability boundaries across diverse DT domains, confirming its indispensable role as a non-intrusive, broadly applicable event-triggering foundation for the broader smart manufacturing ecosystem. Full article

Increasing penetration of electric vehicles, heat pumps, and rooftop photovoltaics is creating thermal and voltage stress in low-voltage distribution grids. This work links the German Federal Government energy transition pathway (2025–2045) with state estimation performance requirements, evaluated at five milestone years from 2025 to 2045 on two SimBench reference networks across three equipment size levels (large, medium, small) and three VDE Forum Netztechnik/Netzbetrieb (VDE FNN) measurement constellations that differ in the availability of transformer- and feeder-level instrumentation. Within this work’s analysis, congestion is caused exclusively by transformer overloading and voltage-band violations. No individual line exceeds its thermal rating (maximum: 98.6%). Equipment size governs congestion onset for a given deployment trajectory: under large equipment, congestion remains absent through 2045, under medium equipment it emerges from 2035 (4 of 10 scenarios), and under small equipment from 2025 (9 of 10). Without transformer instrumentation, median voltage estimation errors reach 6–42% regardless of smart meter penetration. Adding a single transformer measurement reduces errors by an order of magnitude, achieving median errors of 0.5–1.4%. In urban networks, transformer-level instrumentation meets the VDE FNN voltage accuracy target (99th percentile voltage error below 2%) in all configurations. In rural networks under small equipment, the target is approached but not met. These findings motivate prioritizing transformer instrumentation as an effective first step for grid observability and supplementing the current consumption-driven metering rollout with risk-based deployment criteria linked to local congestion exposure. Full article

Considering that smart meter roll-out has already been completed in several European countries for some years now, this review assesses the current state and future opportunities for the direct integration of commercial wallboxes and smart meters in Europe. Despite successful smart meter roll-outs, direct integration remains challenging: while commercial wallboxes are sold on international markets and follow recognized standards, installed smart meters and related cloud platforms are mostly national or regional products, and grid operators have developed proprietary technologies to support their own Advanced Metering Infrastructures (AMI). Here, we first advocate the case for direct integration, noting that it is particularly well suited for local load management when EVs are the only flexible loads and for the provision of novel flexibility services based on real-time grid signals. We then review smart meters data exchange protocols and communication interfaces and identify the common issues hindering effective smart meters exploitation. We eventually propose a set of recommendations to tackle current smart metering infrastructures limitations and unlock their identified potential. Full article

This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation. Full article

Accurate electricity consumption forecasting is essential for the efficient planning and operation of modern power systems. The development of predictive models based on machine learning and deep learning strongly depends on the availability of well-documented and publicly accessible electricity consumption datasets. However, most existing databases are concentrated in Europe and North America and are typically focused on residential measurements obtained from smart meters, resulting in limited representation of equatorial regions. This work presents a structured review of public electricity consumption repositories, analyzing characteristics such as geographical coverage, temporal resolution, user type, and accessibility. Based on the limitations identified in the literature, a new electricity consumption dataset obtained from real measurements collected at distribution substations located in an equatorial region is presented. The dataset was organized through a systematic preprocessing workflow that included temporal standardization, construction of 48-h sliding windows, normalization, and stratified partitioning into training, validation, and test subsets. The descriptive statistical analysis confirms the consistency of the generated subsets and reveals differences between working-day and non-working-day consumption patterns. The proposed dataset provides a reproducible resource for the development and evaluation of multi-horizon electricity demand forecasting models, as well as for load analysis and energy management studies in equatorial regions. Full article

Thailand’s Provincial Electricity Authority (PEA) is rolling out Advanced Metering Infrastructure (AMI) under its smart-grid initiative, requiring a reliable last-mile wireless network across heterogeneous propagation environments. Narrowband IoT (NB-IoT) is a leading candidate, but per-area deployment decisions have lacked a data-driven framework anchored to measured Thai propagation. Building on our sixteen-site composite-channel characterisation, this study presents a machine-learning feasibility-prediction framework integrating measured channel parameters (n, σ_sh, $\widehat{m}$), an OpenStreetMap-derived synthetic meter-density layer, and a benchmark of Random Forest, Gradient Boosting (GB), and Multi-Layer Perceptron classifiers trained on Monte-Carlo coverage labels to predict 95% RSRP-coverage feasibility per spatial cell. Across 411 cells from four Thai sites spanning Urban Dense, Urban Outdoor, Suburban, and Rural environments, GB achieves accuracy 0.971 and F1 0.969 at 1.7 ms inference latency—four orders of magnitude faster than direct Monte-Carlo simulation. The ML predictor approximates the Monte-Carlo engine under the assumed composite-channel model. A theoretical LPWAN comparison places NB-IoT as recommended for Suburban and Rural AMI; Suphan Buri (Rural) is the only RECOMMENDED case (88.5% cells feasible), with hybrid PLC backhaul suggested for dense urban areas. Full article

Non-intrusive load monitoring (NILM) is essential for smart grid demand-side management and energy conservation, yet existing methods suffer from limited feature discrimination, ambiguous identification of similar electrical appliances, and difficulty balancing model accuracy and lightweight deployment. To address these issues, this paper proposes a dual-branch lightweight load identification method fusing steady-state features and lightweight network. Firstly, V-I trajectory images are generated via standardized transformation and two-dimensional histogram logarithmic mapping, while steady-state characteristics, including active power, reactive power, trajectory area and intermediate section slope, are extracted. Then, a dual-branch network is constructed, where the visual branch adopts depthwise separable convolution and lightweight multi-head attention to mine global trajectory features, and the numerical branch uses fully connected layers to encode steady-state features; feature concatenation fusion is adopted to complete appliance classification. The experimental results on the Plug Load Appliance Identification Dataset (PLAID dataset) show that the proposed method achieves a recognition accuracy of 95.35% with only 0.17M parameters, outperforming standard and medium convolutional neural network (CNN) models. Ablation experiments verify that steady-state feature fusion effectively improves the identification accuracy of easily confused and small-sample loads. The proposed method realizes high-precision and lightweight load identification, which is suitable for edge deployment in smart meters and has practical application value for intelligent power management. Full article

Smart-city advanced metering infrastructure enables utility-scale remote analytics, but some forms of under-registration closely resemble lawful changes in demand and are hard to model as anomalies. We study a narrow, physically motivated event family at the single-meter level, namely multiplicative under-registration with unknown onset (a shunt-like attack), in which recorded active energy is approximately scaled by a factor $\alpha <1$ after a change-point while the daily-profile structure and spectral shape remain invariant. We formalize the problem and develop a physics-guided detector family based on weighted daily-profile regression (GLS) and its robust variant (RGLS), with quality-control filters, spectral-consistency checks, and an optional reactive-channel gate, designed to stay selective under confounders such as rooftop photovoltaics, electric-vehicle charging, and heat-pump onsets. On a device-disjoint Low Carbon London benchmark (487 households) the preferred GLS detector attains precision $0.915$, recall $0.978$, and $F_1=0.945$ at $\alpha =0.10$ while keeping the non-theft suspected rate near $1\%$; a cross-dataset check on Open Power System Data with real EV/PV/heat-pump overlays yields zero false alarms on all 72 cases, and Mendeley and WPuQ benchmarks add a second large family and a reactive-channel test. We compare against external baselines (classical change-point detection, Isolation Forest, autoencoder, LSTM, gradient boosting, and a supervised statistical pipeline) on the same protocol: generic anomaly detectors fail on this shape-preserving attack, and supervised models match the detector only in-distribution while, unlike it, failing to transfer to real lawful confounders. All metrics carry bootstrap confidence intervals, and a full reproducibility bundle accompanies the submission. Full article