Search Results (113)

Accurate short-term prediction of network service traffic is essential for communication resource allocation and proactive fault warning in smart grids. However, smart grid service traffic is characterized by nonlinear fluctuations, strong spatio-temporal coupling, and considerable uncertainty, making it difficult for existing methods to capture long-range dependencies, adapt to dynamic topological relationships, and reflect prediction risks. To address these issues, this work develops a deep learning framework that integrates a spatio-temporal Transformer with a diffusion mechanism. The spatio-temporal Transformer extracts temporal evolution patterns and spatial logical correlations from historical traffic matrices, while the diffusion module improves robustness to abrupt traffic variations through latent uncertainty modeling. Furthermore, attention-guided recurrent units are used to generate stable multi-step forecasting sequences. Experiments on a real-world network dataset show that, compared with mainstream benchmark models, the proposed framework reduces Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Relative Squared Error (RRSE) by 46.62%, 47.05%, and 44.18%, respectively. These results indicate that the framework improves prediction accuracy and stability while alleviating error accumulation in long-horizon forecasting, thereby providing reliable technical support for smart grid network management. Full article

The convergence of Software-Defined Networking (SDN) and Blockchain (BC) creates a symbiotic relationship in which SDN’s programmable global visibility complements BC’s decentralized, immutable trust model to address critical cybersecurity vulnerabilities and cyber attacks. Addressing the fragmentation in the current literature, this study rigorously investigates BC and SDN (B-SDN) integration with the primary objectives of: (1) differentiating impacts across varied sectors, including the Internet of Things (IoT), Smart Grids, and Vehicular Ad Hoc Networks (VANETs) and more; (2) analyzing critical performance metrics such as energy efficiency and scalability; (3) classifying mitigation, detection, and prevention schemes for specific threats; (4) examining novel Artificial Intelligence (AI) methods; and (5) identifying open challenges and future research directions. Methodologically, this study conducts a survey of state-of-the-art B-SDN studies to investigate six key areas: Industry-specific applications, security mechanisms, defense strategies, defenses against specific attacks, AI integration, and implementation performance. The findings demonstrate that B-SDN integration shows strong potential in simulated and prototype environments to mitigate specific high-impact threats, such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), and spoofing, across various domains, including IoT, 5G/6G, VANETS, and Smart Grid. Despite the benefits and advantages promised by B-SDN, several limitations continue to exist, including the latency–security trade-off inherent to consensus protocols and scalability constraints in large-scale deployments. Finally, open research challenges persist in AI-driven automation, particularly in Federated Learning (FL) and in the development of standardized interoperability protocols required to enable the transition from conceptual models to operational systems. Full article

Grid-connected photovoltaic (PV) systems integrated into industrial and institutional buildings are critical components of sustainable built environments, where accurate real-time power estimation underpins smart energy management, demand–supply balancing, and reduced dependence on the utility grid. This study develops and validates an Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting of the flexible power point (FPP) in a 117.76 kWp rooftop PV plant serving a technical workshop facility in northwestern Algeria. The proposed model uses environmental inputs (solar irradiance, ambient temperature, module temperature) and electrical inputs (load power, grid power) acquired from a supervisory monitoring infrastructure to predict the PV system’s FPP under real operating conditions in the built environment. A dataset of 24,479 valid samples spanning 85 distinct calendar days (1 May to 24 July 2025) was collected and preprocessed through cleaning, filtering, and feature-specific normalization. To ensure rigorous out-of-sample evaluation, three complementary validation strategies were implemented: (S1) a random day-based split (60 train/11 test days), (S2) a strictly chronological 70/15/15% split (50/11/10 days), and (S3) an external 14-day hold-out (11–24 July 2025) excised before any training, tuning or model selection step. Statistical analysis reveals strong nonlinear dependence of PV power on solar irradiance and module temperature, with correlations $r\approx 0.93$ between irradiance and module temperature, $r\approx 0.82$ between irradiance and PV power, and $r\approx 0.95$ between load and grid power, highlighting the importance of accurate predicting for facility-level energy management. The ANFIS model achieves $R^2=0.9992$, RMSE $=653.62$ W and MAE $=276.90$ W on the random-split test set; $R^2=0.9998$, RMSE $=325.40$ W and MAE $=119.17$ W on the chronological test set and $R^2=0.9997$–$0.9998$, RMSE $=363.45$–$408.50$ W on the external 14-day hold-out that was never seen during training. Comparative experiments with k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and a Deep Neural Network show that ANFIS is the only model maintaining sub-700 W RMSE on every split, whereas all five benchmarks degrade sharply under chronological and external evaluation (e.g., SVM 2225 → 5198 W; Decision Tree 7440 → 8058 W; DNN 1576 → 2576 W). The persistence of test/external RMSE below the training RMSE on data never used during model construction empirically rules out data leakage as a cause of the high accuracy. These results demonstrate that the proposed, interpretable neuro-fuzzy framework offers a robust and accurate tool for PV power estimation in building-integrated systems, supporting smart energy management and improved performance of energy-intensive built environments. 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 growing use of electric vehicles (EVs) has created a strong need for smart, data-driven charging management systems that can support large-scale and sustainable infrastructure. This study introduces a modular cloud-based framework that combines artificial intelligence and machine learning to provide predictive insights for energy demand and station expansion. The system mainly consists of two complementary models. The first is an AutoRegressive Integrated Moving Average (ARIMA) model that forecasts charging energy demand using transactional data from Palo Alto. The second is a Light Gradient Boosting Machine (LightGBM) model that predicts optimal charging-station locations using spatial data from the U.S. Department of Energy’s Alternative Fuels Data Center (AFDC). Both models were deployed as scalable containerized microservices and were validated for accuracy and efficiency within the cloud environment. This proposed framework establishes a predictive link between energy-demand trends and infrastructure planning. It demonstrates the viability of cloud-native, AI-enabled systems to proactively manage EV charging networks and future smart-grid applications. Full article

This study proposes a Long Short-Term Memory (LSTM)-based deep learning model for short-, medium-, and long-term forecasting of solar energy production. Approximately four years of hourly data from four photovoltaic power plants in Karabük were used. In addition to production data, meteorological and environmental variables were included through a multivariate forecasting approach. The model was tested under three scenarios at different time scales. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R²) metrics. Results showed high prediction accuracy, particularly with seasonal data, where R² values exceeded 0.90 in most cases. In forecasts based on monthly data, performance was more variable, and the shorter data window limited the model’s learning capacity. Long-term analyses indicated that the model successfully captured overall production trends and achieved high accuracy across all Photovoltaic (PV) systems. The findings also revealed that incorporating meteorological and environmental variables significantly improved prediction performance. In particular, air pollution parameters were effective in long-term production forecasting. Overall, the study demonstrates that Long Short-Term Memory (LSTM)-based models are reliable and effective tools for solar energy forecasting, with strong potential for energy planning and smart grid applications. Full article

Accurate residential load forecasting is crucial for enhancing the efficiency and reliability of energy systems in smart grid and demand response applications. However, residential load data are characterized by strong stochasticity, high volatility, and pronounced multi-scale temporal dynamics while being highly susceptible to noise and outliers. These challenges hinder existing methods from effectively capturing complex temporal patterns and learning reliable inter-variable dependencies, thereby limiting forecasting accuracy and stability. To address these issues, this paper proposes a Prior-Guided Multi-Scale Neural Network (PG-MSNN) for multi-step residential load forecasting. The proposed framework integrates prior-guided dependency modeling with multi-scale temporal representation learning in an end-to-end trainable architecture. Specifically, a learnable periodic prior space is constructed, within which a Prior-Guided Module (PGM) is designed to learn cross-variable dependencies and provide structured global periodic guidance. In parallel, a Multi-Scale Patch-LSTM Encoder (MS-PLE) is developed to model temporal dynamics across multiple scales through patch-based sequence representation and adaptive cross-scale fusion. Extensive experiments on three real-world datasets, including IHEPC, REC, and CN-OBEE, demonstrate that, under within-household temporal forecasting settings, the proposed method achieves consistent and competitive performance across various forecasting horizons. Full article

This work is devoted to the application of complex-valued neural networks based on the multilayer neural network with multi-valued neurons (MLMVN) for short-term electrical load forecasting in smart grid energy systems. Accurate forecasting is a critical component of energy management systems, as it directly impacts the efficiency of control and optimization strategies in increasingly distributed and stochastic environments. The proposed approach leverages the intrinsic properties of complex numbers to model periodicity and nonlinear relationships typical of load time series. A compact feedforward architecture with two hidden layers is adopted and combined with multiple preprocessing strategies, including unit circle encoding, Fourier transform representations, and hybrid feature mappings incorporating temporal information such as the day of the week. The performance of the proposed models is evaluated on real-world prosumer data and compared against two benchmarks: a seasonal persistence model and a Long Short-Term Memory network. Results show that MLMVN-based approaches achieve comparable or improved performance in terms of RMSE and error reduction capability, despite their lower architectural complexity. Fourier-based preprocessing methods demonstrate strong effectiveness in capturing underlying temporal patterns. These findings suggest that complex-valued representations provide a promising alternative to traditional deep learning approaches, offering a favorable balance between accuracy, interpretability, and computational efficiency in Smart Grid forecasting applications. Full article

This paper introduces a reliability-centric stacking framework for smart-grid stability prediction, considered here as the binary classification of stable versus unstable operating conditions in a benchmark smart-grid dataset. A key methodological strength of the proposed approach is its leakage-safe evaluation protocol, which combines strict outer cross-validation with out-of-fold meta-features so that performance is assessed without reusing validation information. The meta-learner is trained with Tempered Fractional Gradient Descent (TFGD), a history-aware optimizer designed to stabilize meta-level learning and improve the trustworthiness of probability estimates. On the UCI Electrical Grid Stability dataset, the proposed TFGD-stacking framework preserves strong discrimination performance while delivering clear gains in calibration and probabilistic quality compared with soft voting and strong single learners. In particular, it improves Balanced Accuracy and substantially reduces calibration error and proper scoring losses, leading to more reliable probability estimates for threshold-based decision-making. Risk–coverage analysis further shows that these reliability gains translate into better decision support under uncertainty. These findings support TFGD-stacking as a practical and principled approach for smart-grid applications where calibrated probabilities are essential for operational supervision and intervention. Full article

The increasing reliance on smart grid communication systems has significantly raised the demand for robust cybersecurity measures to defend against advanced threats. This paper proposes a two-tier classification framework to enhance the detection and diagnosis of man-in-the-middle attacks within smart grid communication protocols. Initially, the model detects the presence of an attack and then identifies the specific type of man-in-the-middle attack through subsequent inferences. To achieve this, the “Man-in-the-Middle Attacks Targeting Modbus TCP/IP and MMS Protocols in the Smart Grid” dataset was carefully preprocessed and analyzed to better understand the underlying hidden characteristics. This understanding, coupled with existing works on fault detection and diagnosis, facilitated the engineering of new features from the original dataset. Four classifiers were employed in each tier: Random Forest, XGBoost, LightGBM, and CatBoost. The first tier exhibited exceptional performance, with the CatBoost framework achieving 99.6% accuracy. The second tier also demonstrated strong results, with the same model achieving 99.1% accuracy. Systematic model explainability was conducted using SHapley Additive exPlanations for both tiers and revealed that the highest accuracy was achieved using five features for the first and six for the second. The average inference time was approximately 4.76 milliseconds. The proposed framework is accurate, fast, interpretable, lightweight, and well-optimized for direct implementation in smart grid systems to detect and diagnose man-in-the-middle attacks. Full article

The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet the literature is lacking systematic quantitative overviews of its scientific development. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems, enabling a differentiated understanding of research dynamics. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems. The findings highlight priority areas for future research, particularly industrial integration and scalable charging infrastructure, and offer insights for policymakers and industry stakeholders. Full article

Accurate forecasting of wind and photovoltaic power remains challenging due to the strong nonlinearity, nonstationarity, and seasonal heterogeneity of renewable generation series. To address this issue, this study proposes a hybrid forecasting framework integrating time–frequency joint analysis (TFAA), temporal convolutional networks (TCN), long short-term memory (LSTM), and multi-head self-attention (MHSA). Wavelet transform is used to extract frequency-domain representations, which are jointly encoded with the original time-domain sequence through a dual-branch architecture and adaptively fused. The fused features are then processed by a TCN-LSTM backbone to capture both long-range dependencies and short-term dynamics, while MHSA is introduced to enhance global contextual modeling. Experiments on wind-farm and photovoltaic datasets from China, together with external validation on the NREL WIND Toolkit and the GEFCom2014 Solar benchmark, show that the proposed model achieves the best overall seasonal performance and maintains competitive improvements on public benchmarks. Additional ablation studies, repeated-run statistical validation, persistence-based skill-score analysis, prediction-interval evaluation, ramp-event assessment, meteorological-driver enrichment, permutation-based driver attribution, regime-conditioned error diagnostics, and transferability evidence analysis further confirm the effectiveness, robustness, physical consistency, and practical applicability of the proposed framework. The results indicate that the proposed model provides a reliable and operationally relevant solution for short-term wind and photovoltaic power forecasting. These findings further support sustainable renewable-energy integration, smart-grid dispatch, and low-carbon power-system operation. Full article

To deal with the high standards, strong restrictions, and high repeatability that are inside State Grid small-scale infrastructure projects, this research puts forward a norm-driven generative design method, which conquers the low efficiency, compliance dangers, and semantic breakage that are usual in manual modeling. Taking standards such as Q/GDW 11382.3-2015 as the knowledge origin, we construct an ALBERT-BiLSTM-CRF semantic parsing model and change natural-language clauses into executable design restrictions via normative text pre-processing, BIO sequence marking, and rule triplet mapping. Therefore, model training and assessment produce Accuracy, Precision, Recall, and F1 of 98.05%, 95.49%, 95.88%, and 95.59% separately, with 100% precision for logical comparison and conjunction labels; thus, this provides a steady semantic base for the rule base. At the component level, a three-part coding plan and unit module collection are built based on OmniClass and GB/T 51269, which makes semantic consistency and traceability between components and space functions possible. At the system level, a continuous work process is carried out through the Revit API, which covers scheme making, automatic arrangement, and deliverable output. Hence, validation on a real case in a digital operation center for the power system shows that the design time for the third-floor administrative office area was cut from about 20 h to around 4 h, and the first-time solution met all code restrictions, which improves efficiency and compliance in a significant way. The results point out that norm-driven generative design can supply deployable automation and high-quality outputs for small-scale power infrastructure, which provides a sustainable database for digital twins and smart O&M. Full article

Accurate demand forecasting and adaptive load balancing are critical for maintaining stability and efficiency in modern smart power grids. This study proposes a hybrid deep learning (DL) framework, termed Transformer-Generative Adversarial Network-Gated Recurrent Unit (Transformer-GAN-GRU), which integrates global attention-based temporal modeling, generative data augmentation, and sequential refinement into a unified architecture. The proposed framework captures both long- and short-term dependencies while improving representation of imbalanced demand patterns. The model is evaluated on three heterogeneous benchmark datasets, namely Pecan Street, the reliability test system-grid modernization laboratory consortium (RTS-GMLC), and the reference energy disaggregation dataset (REDD). Experimental results demonstrate that the proposed model consistently outperforms state-of-the-art baselines, achieving a maximum accuracy (Acc) of 99.49%, a recall of 99.67%, and an area under the curve (AUC) of 99.83%. In addition to high predictive performance, the framework exhibits strong stability, fast convergence, and low inference latency, confirming its suitability for real-time deployment in smart grid environments. Full article

The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article