Search Results (1,136)

The final stage of urban logistics does not end at the building entrance but continues within complex, vertically structured indoor environments, where conventional ground-based delivery systems face limitations in efficiency, flexibility, and scalability. This study introduces the concept of last-meter delivery, defined as unmanned aerial vehicle (UAV)-enabled transport from the building envelope to the recipient within global navigation satellite system (GNSS)-denied, building-regulated indoor space, and systematically reviews the literature from two traditionally separate domains: indoor-UAV operation in GNSS-denied spaces, and outdoor-UAV-based logistics. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 297 studies are synthesized through a two-stream thematic synthesis. The review makes three contributions. First, a unified analytical framework is developed across four dimensions (spatial mobility, logistical capability, social acceptance, and operational coordination) through which the two bodies of literature are shown to be largely complementary, with the gaps in one stream coinciding with the strengths of the other. Second, indoor aerial delivery is found to be subject to a distinct set of operational constraints, including micro-scale navigation accuracy, strict geometric safety envelopes, close human–UAV interaction, and privacy sensitivity, implying that indoor transport-UAVs cannot be realized through simple miniaturization of outdoor platforms but require precision-oriented, human-centric, and building-aware design. Third, the four dimensions are translated into a building-management-oriented indicator framework covering spatial compliance, handover standardization, building information modeling (BIM) integration, occupant consent, and liability allocation, reframing last-meter requirements in terms that are actionable for building planners and facility managers. By framing these challenges within the last-meter perspective, this review identifies the gap between current last-mile theories and emerging in-building aerial logistics and provides a structured foundation for future research. 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-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs. 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

Advanced Metering Infrastructure (AMI) over 5G New Radio (NR) massive machine-type communication (mMTC) networks require efficient and adaptive communication mechanisms to support reliable data delivery for large numbers of smart meters under dynamic traffic and channel conditions. In this work, we propose a framework in which each smart meter chooses, at runtime, whether to transmit directly to the base station (BS) or via a nearby Data Aggregation Point (DAP). The optimal choice is dynamic and depends on DAP buffer occupancy, periodic congestion, channel quality, and packet deadline pressure. Formulating this as a per-meter binary decision yields an action space of size $2^N$ for N meters, which is intractable for reinforcement learning (RL). We reformulate the problem as regional strategy composition: the RL agent selects one parameterized association strategy for each DAP region from a small library of interpretable rules, and a deterministic mapping expands the regional choice into per-meter modes. It reduces the policy action space from $2^N$ to $K^D$, where D is the number of DAPs and K the number of strategies, while preserving meter-level control granularity. We evaluate Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) controllers against eight meter-level baselines on a 5G NR-calibrated simulator with 1500 m, six DAPs, deadline-bounded delivery, stale channel-state information, and phase-offset congestion cycles. Across three traffic regimes and five random seeds, PPO improves packet delivery ratio (PDR) over the strongest heuristic by +0.63, +2.41, and +2.66 percentage points under baseline, high-load, and bursty-cycle conditions, respectively; all gains are statistically significant (paired t-test, $p<0.001$; Cohen’s d up to 5.12), and the advantage grows with traffic stress. The results show that learned regional composition of classical heuristics outperforms any single fixed heuristic precisely when no individual rule is globally optimal. Full article

Post-meter water leakage management is critical because leaks occurring after the subscriber meter often remain undetected for long periods, resulting in substantial water wastage and increased abstraction, treatment, and pumping requirements. This study presents a pilot application in Antalya, Türkiye, covering 29 public areas, including schools, mosques, cemeteries, parks, public toilets, health service units, and municipal buildings. Smart meters recording 15-min interval data over nine months were used to distinguish water consumption from post-meter leakage. The analysis revealed high leakage volumes, particularly in schools (7955 m³), cemeteries (3233 m³), and mosques (2721 m³), with the highest leakage ratio observed in mosques (0.77). Overall, post-meter leakages significantly increased operational costs, energy use, and associated greenhouse gas emissions. Full article

Ports combine clusters of operational buildings, shared energy infrastructure, and structurally critical assets requiring coordinated management to ensure safety and efficiency. Nevertheless, existing Digital Twin (DT) frameworks for building energy management rarely integrate Structural Health Monitoring (SHM) with energy performance assessment, while port-specific implementations remain scarce. This paper presents a pre-operational energy-aware DT architecture for port building clusters, structured in a unified five-layer framework integrating three capabilities: (i) EGMS/InSAR-based SHM screening with planned in situ sensing and computer-vision inspection workflows; (ii) smart metering and measurement and verification (M&V) protocols aligned with ISO 50001/50015 and IPMVP standards; and (iii) weighted multi-criteria prioritization considering structural condition, energy saving potential, service continuity, and cost. The framework is applied to the Port of Formia (Italy), a brownfield district comprising nine buildings (3371 m²), 16 high-mast lighting towers, shore power infrastructure, and 90 kWp of planned photovoltaics. In the absence of operational metering, energy and carbon values are reported as bounded ex-ante scenario estimates, not as verified performance outcomes. The analysis estimates photovoltaic generation of 116–137 MWh/year and lighting retrofit savings of 31.5–36.8 MWh/year; the related carbon values are treated as gross grid-displacement upper bounds pending measured self-consumption and export data. A four-phase validation roadmap with quantitative acceptance criteria supports the transition from feasibility assessment to verified performance. Full article

Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics. Full article

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

SWANP-AI (Smart Water Network Partitioning with Artificial Intelligence) is a web application with AI natively embedded in its core engines for automated Water Network Partitioning (WNP) of water distribution networks. It is presented as the web-based evolution of SWANP 4.0, whose computational routines have already been tested in operational and research applications. The paper clarifies the full development chain of the platform, from graph-based grouping of candidate District Metered Areas/pressure management Areas (DMA/PMA) to multi-objective boundary pipe optimization and operational decision support. The methodology combines spectral and multilevel k-way partitioning for district generation, NSGA-II for cost–resilience boundary selection, hydraulic simulation through EPANET/WNTR, and AI-supported modules for solution interpretation, sensor placement, natural language editing, and Bayesian leak localization. The application to a real water distribution network shows that SWANP-AI can transform natural language engineering requests into formal optimization tasks, identify hydraulically meaningful candidate interventions, and select balanced solutions through Utopia point analysis, thus reducing manual trial-and-error in DMA/PMA design. The main contribution is a structural generative AI workflow that supports engineers not only in analyzing a network as it is, but also in designing how the network should be partitioned and operated. 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

This study investigates wavelet-based compression of smart-metering data transmitted through a software-defined radio chain implemented in LabVIEW with QPSK modulation and USRP platforms. The objective is to reduce the transmitted payload while preserving the fidelity of the reconstructed electrical load profile. The work combines a mathematical formulation of the DWT-based compression and reconstruction process, a controlled scenario evaluation, and an experimental validation on an SDR testbed. The scenario analysis shows that the compression–reconstruction trade-off is best achieved in an intermediate operating region, where excessive coefficient removal increases reconstruction error despite higher nominal reduction. In the laboratory SDR campaign, Haar wavelet order 1 at the LabVIEW coefficient-retention setting 59 was selected as the most balanced representative configuration, achieving a 60.2% unit-based compression ratio, 10.61% relative error, $\mathrm{RMSE}=31.86$ and $\mathrm{SNR}=16.98\mathrm{dB}$. This selection refers to the physical SDR implementation and should not be confused with the public-dataset validation, where bior4.4 level 8 with 40% retained coefficients provided the best offline compression–reconstruction trade-off. Under the tested USRP/LabVIEW configuration, the 5 GHz setup showed shorter channel occupation time than the 915 MHz setup, with lower measured coverage in the same laboratory campaign. The additional validation using the public UCI Individual Household Electric Power Consumption dataset confirmed that DWT compression can preserve load-profile structure under substantial coefficient reduction. Overall, the results indicate that wavelet compression is technically feasible for smart-metering transmission over SDR links when the wavelet family, order, coefficient-retention setting, and radio-link operating conditions are jointly considered. Full article

Smart meters increasingly operate as grid-edge sensing and communication nodes, extending their role beyond conventional digital billing by generating records for local energy trading. In such settings, smart meter-derived records may support coordination, participant interaction, validation, billing, and settlement across different trading architectures. Once these records leave the metering edge, their security and privacy risks depend on how they are routed, reused, protected, and interpreted across centralized, transactive, and peer-to-peer trading workflows. In this review, we examine smart meter-based energy trading through a record-centric and framework-oriented lens. We first clarify the role of smart meters and smart meter-derived records, then compare three representative trading frameworks in terms of data-path structure, coordination pattern, trust organization, and validation or settlement positioning. Building on the comparison, we identify three lifecycle-based layers of issues: record integrity and temporal consistency, insecure transmission and interface access security, and confidentiality and privacy exposure. We also review existing mitigation mechanisms and remaining limitations for each issue layer. We conclude that future work should prioritize lifecycle-wide record governance, temporal continuity, privacy–accountability co-design, and deployable protection across hybrid trading environments. Full article