Search Results (207)

The increasing diffusion of Renewable Energy Communities offers new opportunities to support vulnerable households through locally generated renewable energy. However, current Home Energy Management Systems mainly optimize energy efficiency and cost reduction, while providing limited support for protecting critical household loads under constrained energy availability. This paper proposes an AI-based Energy Guardianship framework that combines a commissioning phase, in which a Local Appliance Atlas is created from the electrical signatures of the appliances actually installed in a specific dwelling, with an online phase that identifies operating appliances from aggregated measurements and dynamically allocates available energy according to appliance priority. Appliance identification is performed using rich electrical signatures including transient behavior, dynamic V-I trajectories, harmonic information, power profiles, and conventional electrical features extracted from aggregate voltage and current measurements. Unlike conventional home energy management systems, where appliance identification is mainly used to optimize energy consumption, the proposed framework exploits NILM information to support socially aware decisions that preserve critical services while delaying or limiting non-essential loads. A low-cost monitoring architecture is developed to recognize household appliances through electrical signatures and classify loads according to their criticality. When power thresholds are approached, the system recommends demand-side actions, postpones non-essential consumption, and protects critical devices. Preliminary simulation scenarios demonstrate the feasibility of the proposed framework in protecting vulnerable users under limited energy availability while simultaneously improving photovoltaic self-consumption and reducing dependence on grid energy. Although optimization is not the primary objective, the framework naturally supports renewable-aware energy scheduling and future interaction with energy service providers. Full article

This paper proposes a personalized federated learning framework for appliance recognition in non-intrusive load monitoring (NILM) to address real-world data heterogeneity. Each client maintains a personalized model alongside shared global components. To decouple these components, a context-aware conditional policy module adaptively separates global and personalized information via learnable gating. The method enables collaborative training without raw data exchange and mitigates the impact of inter-client label distribution skew. We evaluate the proposed method under four federated settings: independent and identically distributed (IID), Dirichlet non-IID, house-partitioned, and leave-one-house-out. Experiments on three public datasets show strong and stable performance compared with existing federated approaches. Under Dirichlet skew ($\alpha =0.1$), our method achieves an accuracy of $93.8\pm 0.9\%$ on PLAID, $92.3\pm 1.4\%$ on WHITED, and $96.6\pm 1.5\%$ on COOLL. In the leave-one-house-out setting, it attains $80.3\pm 2.0\%$ on PLAID. These results demonstrate the effectiveness of the proposed method across challenging non-IID scenarios. Full article

This paper presents a digitally controlled current-mode synaptic weighting cell in 28 nm FD-SOI CMOS for low-complexity Multi-Layer Perceptron (MLP) inference in event-based Non-Intrusive Load Monitoring (NILM). A compact bias-free [16,16] MLP is trained offline by backpropagation for fixed-weight feedforward inference. Using 616 ON/OFF events extracted from high-frequency REDD measurements, six appliance classes are characterized by four event-level features: active-power variation, reactive-power variation, current total harmonic distortion of the differential event signature, and event interval. With 16-level input quantization, the model achieves 92.9–93.4% test accuracy and 90.5–90.7% test Macro-F1, requiring 320 weighted-sum branches across two hidden layers. A four-input first-hidden-layer weighted-sum unit is selected as a representative circuit instance. Its computation is mapped to bounded current ranges using current-coded inputs, an 8-bit magnitude-controlled current-mode multiplier, sign-bit current steering, differential current accumulation, and signed-current scaling. The circuit contribution focuses on the weighted-sum datapath, particularly the repeated synaptic weighting cell; activation and complete classifier implementation are outside the scope of this work. Transistor-level PVT and supply-variation simulations validate the signed weighted-sum path, while post-layout extraction evaluates the repeated multiplier cell. The results demonstrate the block-level feasibility of digitally programmable current-mode synaptic weighting for compact event-based NILM inference. Full article

Non-intrusive load monitoring (NILM) enables appliance-level disaggregation from a single household meter, yet existing Seq2point-based methods are plagued by inadequate noise robustness, unsatisfactory state recognition, and limited cross-dataset generalization. This paper proposes a denoising multi-task convolutional neural network that fundamentally differs from prior approaches by coupling a denoising autoencoder with task learning through a shared reconstruction head—rather than treating denoising as an isolated preprocessor or simply stacking independent loss branches. This design forces the shared feature extractor to preserve fine-grained temporal signal fidelity while jointly optimizing power regression and state classification, thereby imposing an implicit regularization that suppresses noise interference and enhances transferable representation. The model is evaluated on UK-DALE and REDD datasets, achieving MAE/F1 scores of 12.91 W/84.75% and 5.02 W/96.99%, respectively. Ablation studies confirm the synergistic gains from the joint reconstruction–regression–classification paradigm. Furthermore, statistical analysis across five typical appliance types (e.g., kettle, washing machine, and refrigerator) against three state-of-the-art Seq2Point variants reveals that the proposed method yields consistently superior MAE and F1 improvements with statistical significance (paired Wilcoxon test, p < 0.05) and markedly lower performance variance, demonstrating robust efficacy across diverse load profiles. These results substantiate the proposed model as a reliable and statistically validated solution for fine-grained residential energy management in smart grids. Full article

Human papillomavirus (HPV)-associated cervical lesions remain a significant disease burden and minimally invasive blood-based biomarkers could complement cytology and HPV testing. This study aimed to characterize the proteomic composition of plasma-derived CD9⁺ small extracellular vesicles (sEVs) across the morphological spectrum of HPV-associated cervical disease, from histologically normal (NILM) through low-grade (LSIL) and high-grade (HSIL) lesions to invasive squamous cell carcinoma (SCC). Plasma samples from 34 women (NILM, LSIL, HSIL, SCC) were pooled per group, and CD9⁺ sEVs were isolated using an electrochemically controlled immunoaffinity capture method, followed by nanoparticle tracking analysis, transmission electron microscopy, Western blotting, and label-free LC–MS/MS proteomic profiling. The core sEV proteome comprised 258 shared proteins. LSIL showed the most pronounced changes with broad enrichment of complement and coagulation components and acute-phase reactants alongside depletion of immunoglobulin chains and complement C1r-like protein (C1RL). HSIL exhibited few differential proteins, dominated by neutrophil degranulation and retinoid metabolism pathways. SCC demonstrated extensive cargo depletion (22 downregulated proteins) and a nearly sevenfold upregulation of C1RL. Five proteins (including immunoglobulin chains and GPLD1) correlated positively with lesion severity. Pathway analysis consistently implicated platelet activation, lipoprotein remodeling, and insulin-like growth factor signaling. We conclude that plasma CD9⁺ sEVs carry stage-specific proteomic signatures distinguishing HPV-associated cervical lesions, with C1RL emerging as a candidate biphasic marker warranting further validation. 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

To enable fine-grained electricity management on the user side under the dual-carbon strategy and address the inherent limitations of traditional non-intrusive load monitoring (NILM) methods in multi-load parallel operation scenarios, this paper proposes a novel synergistically optimized framework. The framework sequentially integrates three core modules to tackle the key challenges of load identification: SSA-VMD-based load quantity estimation, CNN-LSTM-Attention-based current separation, and GAF-ResNet18-based load recognition. First, the Sparrow Search Algorithm optimizes Variational Mode Decomposition parameters, combined with Pearson-PCA, to accurately estimate the number of operating loads in mixed-power signals without prior knowledge. Second, a hybrid CNN-LSTM-Attention model extracts deep spatial-temporal features from the aggregated current spectrogram, enabling high-fidelity separation and reconstruction of individual load current waveforms. Third, the separated current signals are transformed into Gramian Angular Field images and classified by a ResNet18 network for robust load identification. The framework’s efficacy is rigorously validated on both the public PLAID dataset and a self-constructed laboratory dataset, covering diverse dual-load and triple-load operating conditions. Results demonstrate that the method achieves R² coefficients exceeding 0.9 for current waveform reconstruction and maintains load recognition accuracy above 91% across all test cases, significantly improving identification performance under complex electricity consumption conditions. This high-performance load disaggregation provides critical data support for advanced grid applications, including demand response, load forecasting, and distribution network planning, thereby contributing to the intelligence and efficiency of future power systems. Full article

Non-intrusive load monitoring (NILM) estimates component-level energy use from aggregate measurements, but low-frequency data limit appliance signatures and make overlapping or weakly observed loads difficult to separate. This paper proposes a relation-aware multi-driver pipeline for interpretable low-frequency load attribution under partial observability. The method does not require supervised component labels or predefined appliance models. It combines semantic feature typing, heterogeneous relation discovery, feature-family construction, mechanism-aware evidence modeling, conservative allocation, event-background separation, and role-based attribution. Only evidence-supported load is assigned to feature families, while unsupported variation is retained as unexplained demand or residual load. The method is evaluated in a simulated EV-focused building case and through measured-building validation on nine ADRENALIN buildings. In the EV case, the selected EV-aligned family achieved a correlation of 0.990 and an NMAE of 0.100 against the withheld EV reference, while heat-pump and base-load recovery was weaker, with NMAE values of 0.565 and 0.895. In the ADRENALIN validation, temperature-associated families achieved median NMAE values of 0.594 using the restricted feature set and 0.576 using the full feature set. Additional comparison, ablation, sensitivity, diagnostic, and runtime analyses show that the pipeline is most effective for dominant event-driven loads, remains limited for smoother or masked lower-magnitude components, and treats unexplained variation explicitly. The results demonstrate a practical framework for interpretable driver-based load attribution when component labels are unavailable or incomplete. 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

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

Cervicovaginal dysbiosis is an established co-factor of high-risk human papillomavirus (HPV) persistence and cervical neoplastic development, yet most studies address the bacterial compartment in isolation, leaving fungal communities and bacterial–fungal cross-kingdom interactions underexplored, particularly in Central Asian populations. We performed shotgun metagenomic sequencing (mNGS) of cervicovaginal samples from 311 Kazakhstani women undergoing routine cervical screening. HPV status was determined using combined PCR and mNGS methods, and cervical screening was completed using liquid-based cytology (NILM, ASC-US, LSIL, ASC-H). Bacterial, viral, and fungal taxa were profiled from a single shotgun dataset with Kraken2 pipeline. Bacterial community state types (CSTs) were determined based on dominant bacterial species, functional gene content was annotated against KEGG using eggNOG, and covariate-adjusted associations were estimated using MaAsLin3. Mycobiome β-diversity differed significantly by HPV status (p = 0.003). In particular, Candida positivity was significantly associated with HPV presence and with high-risk α-9 HPV in cytologically normal (NILM) samples (OR = 3.6, [1.6–9.6], p ≤ 0.001). Covariate-adjusted analysis was consistent with this positive association (q < 0.05). Concurrently, among CSTs, Lactobacillus iners-dominated CST III and dysbiotic Gardnerella vaginalis-dominated CST IV showed a 3-fold higher Candida albicans prevalence (p < 0.01). Further analysis demonstrated that, functionally, both of these CSTs had depleted capacity for lactate metabolism (ko00620, p < 0.0001) and, in particular, for the genetic capacity for pyruvate-dependent H₂O₂ generation (half that of the L. crispatus-dominated CST I). These findings support L. iners as a metabolically permissive rather than protective Lactobacillus and suggest cross-kingdom functional signatures as candidate biomarkers for HPV acquisition and persistence in Central Asia, a region previously absent from the cervicovaginal microbiome literature. Full article

Non-intrusive load monitoring (NILM), as a key technology for decomposing power loads by analyzing aggregate electrical signals, holds significant importance for advancing refined energy management and achieving carbon peaking and carbon neutrality goals. This paper systematically reviews the technical processes of event-based and state-based NILM methods. It focuses on analyzing key technical challenges in typical application scenarios, such as real-time feedback, energy efficiency optimization, and demand response. These challenges include balancing high real-time performance with accuracy, leveraging edge computing while ensuring privacy protection, and addressing issues like unknown load identification and user behavior modeling. Furthermore, this paper discusses cross-cutting challenges related to data quality, algorithm transferability, system integration, and cost. This review aims to provide a systematic, scenario-based analytical framework to facilitate the transition of NILM from theoretical research to practical application, offering insights for subsequent technological development and engineering implementation. Full article

Non-Intrusive Load Monitoring (NILM) represents a powerful approach for energy disaggregation, which enables detailed insights into energy consumption patterns without requiring extensive sensor deployment. While significant advances have been achieved in residential NILM applications, commercial and industrial buildings remain largely underexplored despite their substantial contribution to global energy consumption. This study addresses this gap by developing and evaluating multiple artificial intelligence approaches for energy disaggregation across residential, commercial, and industrial buildings under a unified experimental protocol. We implement and compare several AI-based models, including Vision Transformer (ViT), Variational Autoencoder (VAE), Random Forest (RF), and custom architectures inspired by TimeGPT and Prophet, alongside traditional baseline methods. The proposed framework is validated using three benchmark datasets representing residential (AMPds), commercial (COmBED), and industrial (IMDELD) environments. Experimental results demonstrate that architecture–load interactions, rather than model complexity alone, are the primary determinants of disaggregation accuracy: the ViT-small configuration achieves superior performance for complex industrial loads with R² values exceeding 0.94, Random Forest proves most effective for finite-state commercial HVAC systems with R² up to 0.97, and the Prophet-inspired model excels in capturing seasonal patterns in residential appliances. These findings provide evidence-based guidelines for selecting appropriate AI models based on load characteristics, signal-to-noise ratio, and building type, contributing to the practical deployment of NILM in heterogeneous building environments. Full article

Compressed air leakages represent a major source of energy waste and financial loss in industrial facilities. However, accurately detecting and quantifying these leaks remains challenging due to the wide variation in the accuracy, cost, usability, and practical applicability of available methods. This paper presents a structured review and evaluation of leakage localization and quantification methods for compressed air systems (CASs), categorized into hardware-, software-, and non-technical-based approaches. Based on expert interviews and a comprehensive literature review, a set of evaluation criteria was defined and applied within a multi-criteria decision analysis (MCDA) framework. The Analytic Hierarchy Process (AHP) was used to derive criteria weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the alternatives separately for localization and quantification tasks. To enhance practical relevance, five expert interviews were conducted with industrial stakeholders from diverse professional backgrounds, including maintenance engineers and energy managers. A questionnaire was also distributed to assess the methods. The results provide illustrative insights into the relative suitability of different methods. Within the scope of this exploratory study, from a practical industrial perspective, the compressor duty cycle method and non-intrusive load monitoring (NILM) appear to be promising approaches to leakage quantification, while ultrasonic detection is preferred for localization. Notably, discrepancies between questionnaire-based rankings and expert interview insights highlight the limitations of purely survey-driven evaluations. The proposed framework supports industrial decision-makers in selecting leakage detection and quantification methods by balancing technical performance, implementation effort, and operational constraints, thereby contributing to reduced energy losses and improved system efficiency. Full article

Non-Intrusive Load Monitoring (NILM) systems are increasingly applied in residential and commercial environments to disaggregate energy consumption without requiring additional hardware sensors. The integration of Machine Learning (ML) techniques has enhanced the accuracy and efficiency of load identification and classification in smart meter-based systems. This study presents a systematic review and meta-analysis aimed at identifying, classifying, and quantitatively evaluating ML models applied to NILM. Searches were conducted in the IEEE Xplore and Scopus databases, restricted to peer-reviewed publications from 2017 to 2024. Thirty studies met the eligibility criteria and were included in the quantitative synthesis using a random-effects meta-analysis model (DerSimonian–Laird estimator). The primary effect measure was the F1-score. Statistical analyses were performed using R (version 4.5.0) and Python (version 3.10.0), including heterogeneity assessment and subgroup analyses according to model type. Hybrid models, such as SVDT-KNN-MLP, LE-CRNN, and RBFNN-MOGA, achieved the highest pooled F1-scores, although supported by a limited number of studies. Traditional approaches, including CNN, KNN, and Random Forest, demonstrated consistently strong performance and broader validation, whereas Boosted Trees and RNN-based models showed lower or more variable results. Substantial heterogeneity was observed across studies, highlighting the need for dataset standardization, reproducible evaluation frameworks, and further validation of emerging hybrid architectures in diverse operational scenarios. This study contributes by providing a quantitative synthesis of machine learning models applied to NILM using a structured PRISMA-based methodology and subgroup analysis by model architecture. Unlike previous narrative reviews, this work integrates scientometric analysis with meta-analytic performance aggregation, offering a consolidated and comparative evidence base for future NILM research. Full article