Search Results (99)

Low-elevation Doppler wind lidar scans, known as arc scans, extend traditional vertical-profile measurements to horizontal, off-site locations. This technique is designed to enable measurements at multiple distant locations relative to the instrument. Arc-scan methods have been widely utilized in wind energy applications and are also a promising tool for environmental monitoring for hazard assessment. This method introduces specific challenges absent in traditional vertical profile scans. The limited scan angle restricts the number of wind orientations available for reliable vector extraction. A reliable method of estimating the uncertainty intervals for retrieved wind speed and direction in operational configurations is thus of interest. Here, we developed a closed-form statistical expression for evaluating wind speed and direction prediction intervals. Because rapid-update operational scenarios (such as real-time dispersion modeling) yield a limited number of scans, the framework is specifically designed to remain mathematically robust and computable using only diagonal variance terms, bypassing the need for numerically unstable cross-covariance matrices. The expression was tested against a lidar and sonic anemometry measurement campaign. The wind-arc alignment emerges as a major influencing parameter impacting uncertainty of both direction and speed retrievals. Conclusions regarding scan parameters and siting considerations are drawn. Full article

Static wake steering can increase wind-farm power production, but its application to floating offshore wind farms requires assessment of the coupled wake, platform, structural, and station-keeping response. This study evaluates whether power-maximizing static yaw setpoints identified using the steady, control-oriented FLORIS model retain their benefit when transferred without re-optimization to a coupled FAST.Farm floating wind-farm model. The reference farm comprises four IEA Wind 15 MW turbines mounted on VolturnUS-S semi-submersible platforms. Greedy and static wake-steering operations are compared at three below-rated wind speeds, three sea states, and five matched turbulent-inflow realizations, resulting in 90 farm-level FAST.Farm simulations. Wake behavior is characterized through wake-center deflection, meandering, and velocity-deficit profiles, while turbine and mooring fatigue responses are evaluated using paired damage-equivalent-load statistics. Static wake steering increases mean farm power under all nine investigated wind–wave conditions. The gains are approximately 5.1–$5.2\%$ at $7\mathrm{m}{\mathrm{s}}^{-1}$, $5.0$–$5.1\%$ at $8\mathrm{m}{\mathrm{s}}^{-1}$, and $4.0$–$4.2\%$ at $9\mathrm{m}{\mathrm{s}}^{-1}$, with all paired $95\%$ confidence intervals remaining above zero. The gain results from a power redistribution in which the intentionally yawed upstream turbine incurs a local loss that is exceeded by the combined recovery of the downstream turbines. The fatigue response is strongly component- and turbine-dependent. The paired farm-mean blade-root DEL decreases by $0.82$–$2.24\%$, whereas the tower-base DEL increases by $0.76$–$2.78\%$, and the FairTen1 response generally increases by $0.88$–$2.92\%$. The farm-mean yaw-bearing response is mixed, ranging from a $1.15\%$ reduction to a $4.32\%$ increase. Turbine-level analysis reveals larger localized penalties, reaching approximately $10.4\%$ for the yaw-bearing DEL and $12.8\%$ for FairTen1. Spectral analysis associates the yaw-bearing response with yaw-induced aerodynamic and structural excitation, while the tower-base response is strongly influenced by low-frequency wave–platform dynamics. A complementary FLORIS sensitivity analysis demonstrates that the optimized aerodynamic benefit depends strongly on wind direction, spacing, wind speed, and turbulence intensity. For a Tampen-derived 11-turbine layout, resource weighting over the modeled 4–$13\mathrm{m}{\mathrm{s}}^{-1}$ interval produces an annual energy-contribution increase of $3.653\mathrm{GWh}{\mathrm{year}}^{-1}$, or $0.921\%$. These results provide numerical evidence that static wake steering can retain a positive power benefit in a coupled floating wind-farm environment, but controller assessment must include turbine- and component-specific dynamic loads rather than farm power alone. Full article

A method is proposed for determining the reliability of electrical machines of the most widely used types during their design stage and for verifying this reliability through testing of the components intended for them. The method is invariant with respect to the type of machine and its operating mode. Particular attention is given to its practical application in real engineering practice. The method is applicable to both direct current and alternating current machines, with or without a commutator, operating in either generator or motor mode. It is based on the evaluation of the probability of failure-free operation over a specified time interval, determined on the basis of the reliability characteristics of their windings, bearings, and commutation system. Full article

Most studies of sandy shoreline forecasting address relatively short time scales. Under limited annual observations and strong shoreline persistence, the added value of a Transformer over simple baselines and the influence of remotely sensed shoreline definitions remain insufficiently tested. Using Xichong Beach, Shenzhen, we constructed 40-year shoreline series for 80 transects from 284 quality-controlled Landsat waterlines acquired during 1986–2025. We compared a quality-controlled annual landward-envelope composite waterline with annual median waterlines and examined the effects of transect spacing and positional error on long-term change rates. We then developed a residual spatiotemporal Transformer that uses 10 years of shoreline states and historical wind–wave exposure to directly predict five future horizons, and compared it with persistence, rolling linear trend, and random forest models. The annual landward-envelope composite waterline was systematically landward of the annual median waterline (positional RMSE, 12.78 m), but their alongshore LRR patterns were strongly correlated (r = 0.982) and identified consistent major erosion–accretion zones. After Monte Carlo error propagation, the beach-mean LRR was −0.250 m yr⁻¹ (95% interval, −0.299 to −0.203 m yr⁻¹), whereas the direction of change remained uncertain at 39 local transects. Across 400 year–transect locations in the independent 2021–2025 evaluation period, the Transformer produced the lowest RMSE, MAE, and Dynamic RMSE (9.403, 7.399, and 12.042 m, respectively), with an RMSE skill of 27.0% relative to persistence. Environmental features yielded a small gain during rolling validation but no stable improvement in the independent evaluation period. SHAP attribution identified recent shoreline state as the dominant predictive information, followed by wind–wave exposure. Direct forecasts for 2026–2030 gave a beach-mean displacement of −8.527 m in 2030 (95% conditional residual bootstrap interval, −12.514 to −4.950 m), although every local-transect interval crossed zero. Multi-year predictability is therefore scale dependent: beach-mean trends are more resolvable, whereas local change directions remain constrained by observation error and model residuals. Full article

High renewable penetration makes day-ahead operation sensitive to the directional effects of wind and photovoltaic forecast errors. Conventional prediction intervals mainly evaluate coverage and sharpness, but lower- and upper-boundary violations correspond to different operational risks: shortage-side supply-adequacy pressure and accommodation-side curtailment pressure. This paper proposes context-aware asymmetric conformal quantile regression (CA-ACQR) to construct directional renewable-power risk intervals. The method builds separate conformal scores for the two interval sides, estimates context-dependent boundary corrections, and reallocates the tail-risk budget under supply-priority, balanced, and accommodation-priority profiles. Case studies use regional wind and photovoltaic power data, with contextual groups defined by renewable type, lead-time block, forecast difficulty, weather-risk regime, and output level. CA-ACQR increases the prediction interval coverage probability (PICP) from 91.11% to 94.07% and reduces the accommodation-side violation rate from 4.37% to 1.44%. The results demonstrate selectable directional risk postures and quantify trade-offs among interval width, directional violations, normalized stress cost, and the 95% conditional value-at-risk stress cost. Full article

Reliable medium–long-term wind energy projection is essential in the planning, financing, and operation of large-scale offshore wind development. This study classified projection methods into three categories: statistical/empirical and climate-signal-driven methods, dynamical models with reanalysis and regional downscaling, and machine/deep learning and hybrid methods for bias correction, downscaling, and direct data-driven projection. Then, this study reviewed the technical framework, representative studies, and comparative strengths and limitations. The main finding was that the state of the art increasingly converged on “dynamical simulation plus statistical or machine learning correction”. Next, seven main bottlenecks, along with the countermeasures, were systematically presented: (i) difficult data quality control and insufficient observational representativeness, especially offshore; (ii) divergent, even contradictory, conclusions for the same region across data sources and research groups; (iii) large uncertainty in extrapolating 10 m winds to the continually rising turbine hub height; (iv) difficulty in quantifying and communicating non-stationarity and uncertainty to decision-makers; (v) engineering conversion errors from projected “wind resource” to deliverable “electricity”; (vi) systematic biases in the marine atmospheric boundary layer, strong winds, and extreme conditions; and (vii) unresolved reliability, interpretability, and out-of-distribution generalization of AI models. Correspondingly, three mutually reinforcing strands of countermeasures were proposed: first, strengthening the observational and benchmarking foundation through unified, open, quality-controlled observation networks with data-provenance standards and shared reference datasets and intercomparison protocols; second, advancing physics–data integration and uncertainty quantification through hybrid and physics-informed correction, regime-specific bias correction of boundary-layer and extreme-wind errors, and probabilistic frameworks that delivered and clearly communicated credible intervals; and third, closing the resource-to-electricity gap by embedding power-curve convolution, wake-loss modeling, and availability and technology derating into the projection workflow, with the aim of improving medium–long-term wind energy projection accuracy. Full article

The wind field in urban areas is characterized by an inherent spatial variability, which is also termed spatial uncertainty. This may be manifested as a noticeable difference between rooftop-level measurements in adjacent locations, the degree of which changes throughout the day. In meteorological and environmental contexts, such uncertainty is often described as a probability distribution. Usually, studies deal with the uncertainty of each wind vector component separately, i.e., wind speed and direction. The uncertainty is assumed to be distributed symmetrically around the mean and represented by a single characteristic value. Such representation neglects the correlation between the two wind vector components together. This, in turn, may result in wind vector component combinations that are physically inconsistent with realistic wind regimes. This study proposes a method that quantifies the spatial uncertainty of the urban rooftop wind. It is based on a covariance matrix that quantifies the relationship between the rooftop spatial wind components alongside the seasonal Mahalanobis distance functions. It draws on a representative sample of weather stations and previously calculated seasonal log-logistic Mahalanobis distance functions. Thus, an elliptic-shaped tolerance region is calculated to quantitatively estimate a given proportion of the possible values of the wind vectors at a given time. The model was demonstrated on the metropolitan area of Tel Aviv. The results show that the spatial wind distribution can be very well represented by a small sample of merely four stations. The model’s results were found to be well within the confidence interval, leading to the conclusion that the model is fully capable of providing an accurate description of the current state of the urban wind field. Full article

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative asset management depend on auditable digital representations of turbine behaviour. This study develops a Digital Shadow-based power-curve modelling framework on fourteen years of Supervisory Control and Data Acquisition records from an operational Vestas V52 onshore turbine (850 kW, Dundalk Institute of Technology, Ireland; 457,429 ten-minute records spanning 2006–2020) and benchmarks seven methods under identical preprocessing on a strict chronological hold-out (training 2006–2017; testing 2018–2020; n = 52,388). A parallel random 75/25 split is reported only as a within-distribution diagnostic; it quantifies an optimistic R² inflation of 0.003–0.027 depending on architecture. The Artificial Neural Network attains the best chronological performance (R² = 0.9924, BCa 95% confidence interval 0.9910–0.9931, RMSE = 19.79 kW); only the ANN and a one-dimensional Convolutional Neural Network with twenty-four-step wind-speed lags (R² = 0.9921) deliver clear positive skill against the IEC-style manufacturer power curve. Split-conformal calibration of a Quantile Regression Forest raises empirical 90% prediction-interval coverage from 0.534 to 0.904 at a width inflation from 30 to 51 kW. The framework qualifies as a Digital Shadow and is positioned, through a Horizon Europe Technology Readiness Level audit and an explicit mapping to ISO 50001:2018 Plan–Do–Check–Act energy management and Renewable Energy Community governance under Directive (EU) 2018/2001, as an auditable monitoring layer for cooperative onshore wind operations. The empirical evidence base is a single turbine; multi-turbine, multi-site replication is the natural follow-on validation. Full article

Sand stabilization techniques include mechanical, biological, and chemical methods. Integrated systems combine these approaches in varying proportions. This study tested a sand control system developed by the Kuwait Institute for Scientific Research using a 1/100-scale model in an aeolian sand transport wind tunnel. Experiments employed boundary layer pressure measurements and salti-phone sand transport quantification to examine effects of wind speed, fence height, and tree configuration. Boundary layer velocity was primarily affected by fan speed, with fence height, tree configuration, and measurement location playing minor roles. Sand transport correlated directly with wind speed. Fence height showed inverse proportionality to centerline velocity but direct proportionality off-center velocity. The optimal configuration, i.e., C6 tree spacing (the central row was 35 m from the upwind fence, and subsequent rows were at 5 m and 10 m intervals, using Tamarix aphylla and Prosopis juliflora and with an H2 fence height (1.8 m)), achieved a 68.9% mean sand transport reduction. The graduated vegetation density provided superior momentum absorption versus uniform spacing, while a 1.8 m fence height balanced particle capture against flow blockage. A preliminary economic analysis demonstrates favorable cost–benefit ratios with 2–3-year payback periods. System costs ($185,000/km) are substantially lower than sand removal expenses, providing validated design guidelines for Kuwait and similar arid environments. Full article

For enhancing the level of refinement of short-horizon wind-storage power prediction, this paper introduces an advanced BiLSTM prediction model integrating data preprocessing based on the density-based clustering technique known as DBSCAN, partial least squares regression (PLSR), and particle swarm optimization (PSO). In this paper, “wind-storage power” refers to the net power output of a wind farm integrated with a battery energy storage system (BESS), where the measured data already embed the effects of charge/discharge operations. First, outage and missing data are removed from the historical dataset. DBSCAN is then employed to identify abnormal samples in wind-storage power and meteorological variables, such as wind speed, wind direction, atmospheric pressure, temperature, and humidity, and linear regression is used to correct the detected noise points. Correlation analysis is further conducted to identify the most relevant meteorological inputs, namely wind speed, wind direction, and atmospheric pressure. Next, the PLSR model is applied to generate the preliminary prediction of wind-storage output. On this basis, the BiLSTM network is employed to predict the residual error, which mainly reflects the nonlinear characteristics not captured by the preliminary prediction. Meanwhile, PSO is implemented to determine the most suitable core hyperparameters for the BiLSTM architecture. Ultimately, the preliminary PLSR result is corrected by the predicted residual to obtain the final wind-storage power prediction. The DBSCAN parameters are systematically selected via a k-distance plot (ε = 0.9, MinPts = 2.5), and the PLSR number of components is set to A = 3 based on five-fold cross-validation. Case studies show that, for the 24 h prediction horizon, the proposed method improves prediction accuracy by 2.29%, 11.47%, and 5.54% compared with the BP, Wavelet-LSTM, and standard LSTM models, respectively. Furthermore, statistical significance is confirmed by Diebold–Mariano tests and 10-run confidence intervals. Full article

Industrial predictive maintenance programs often rely on SCADA historian signals characterized by low-frequency sampling and asynchronous reporting intervals. These data constraints, specifically non-uniform scan rates and inter-tag time misalignment, limit the applicability of high-resolution or sensor-intensive prognostic models. This study proposes a lightweight, physics-informed health proxy, the temperature-weighted work (TWW) index, designed to monitor motor stator-winding degradation within these industrial limitations. The TWW index accumulates mechanical work derived from torque and speed measurements, weighted by an adaptive exponential temperature-emphasis function that penalizes operation at elevated temperatures. The formulation is inspired by practical thermal-aging heuristics such as Montsinger’s rule in the qualitative sense that higher temperatures are treated as disproportionately more damaging, but it is not intended as a direct implementation of a fixed absolute-temperature life law. Instead, it is designed as a lightweight adaptive index suitable for online SCADA-based implementation. To address SCADA-specific irregularities, the framework incorporates data synchronization and resampling techniques to align heterogeneous tags, alongside power-thresholding to isolate degradation-relevant load periods. The resulting cumulative index is mapped to a normalized health/RUL proxy using failure-referenced thresholds identified from historical events. Validation using field data from industrial three-phase motors demonstrates that the TWW index provides a monotonic degradation profile that is consistent with documented winding-related failures and proactive removals. Case studies confirm that the model enabled proactive maintenance interventions by signaling the terminal phase of insulation life before catastrophic breakdown, offering a hardware-free and scalable solution for real-time asset management. Full article

Ground-level ozone pollution poses significant risks to public health and ecosystems and remains a major environmental challenge worldwide. Accurate forecasting is difficult due to the nonlinear formation mechanisms of ozone and its strong dependence on meteorological conditions. This study proposes a Wind Speed and Direction-Based Dynamic Spatiotemporal Graph Attention Network (WSDST-GAT) for multi-step hourly ground-level ozone prediction. The model integrates a wind-aware dynamic graph to represent anisotropic pollutant transport and a Transformer-based temporal encoder to capture long-range dependencies. Meteorological variables are incorporated to enhance physical interpretability and predictive robustness. A co-kriging module is further employed to reconstruct continuous spatial ozone fields with quantified uncertainty. Using hourly observations from 35 monitoring stations in Beijing, WSDST-GAT achieves a Coefficient of Determination of 0.957, with a Mean Absolute Error of 5.25 μg/m³, and a Root Mean Square Error of 9.58 μg/m³. The prediction intervals demonstrate strong reliability with a Prediction Interval Coverage Probability of 94.01% and a Prediction Interval Normalized Average Width of 0.174. These results indicate that the proposed framework provides an accurate and physically informed solution for ozone forecasting and air quality management. Full article

To achieve the highest efficiency from the turbines used in wind power plants, the region where the plant will be located must meet the appropriate conditions. One of these conditions, and the most important, is that the wind potential be above the critical value for energy production and be continuous. Locations that meet these conditions contribute positively to energy production and produce high efficiency. Based on the interpreted data, temperature, wind direction, and wind speed data from three turbines located at altitudes of 432, 454, and 492 m in the Sebenoba area of Yayladağ, Hatay, where wind potential is high, were collected at 10 min intervals between 1 January 2017, and 19 September 2018, yielding a total of 50,986 data points. Wind speed was estimated for this region using temperature, wind direction, and time information. Daily, monthly, and seasonal analyses were used to generate forecasts for the three altitudes. Wind speed was estimated using Decision Tree Regression and 10-Fold Cross Validation methods, and Root Mean Square Error (RMSE) values were found to be 0.64917, 0.66629, and 0.59954 for the three altitudes, respectively; the overall RMSE value was found to be 0.60188. RMSE values decreased in daily, monthly, and seasonal analyses, and an inverse relationship existed between wind speed and RMSE. Analysis of these results indicated that the forecast model was suitable. This study supports sustainability by enabling accurate wind speed forecasting for optimal turbine placement, improving energy efficiency, and promoting long-term environmentally and economically sustainable wind energy planning. Full article

This paper proposes a privacy-preserving framework that couples probabilistic wind power forecasting with decentralized anomaly detection in complex power networks. We first design an adaptive federated learning (FL) scheme to produce probabilistic forecasts for multiple geographically distributed wind farms while keeping their raw data local. In this scheme, an artificial neural network with quantile regression is trained collaboratively across sites to provide calibrated prediction intervals for wind power outputs. These forecasts are then embedded into an alternating direction method of multipliers (ADMM)-based load-side dispatch and anomaly detection model for decentralized power systems with plug-and-play industrial users. Each monitoring node uses local measurements and neighbor communication to solve a distributed economic dispatch problem, detect abnormal load behaviors, and maintain network consistency without a central coordinator. Experiments on the GEFCom 2014 wind power dataset show that the proposed FL-based probabilistic forecasting method outperforms persistence, local training, and standard FL in RMSE and MAE across multiple horizons. Simulations on IEEE 14-bus and 30-bus systems further verify fast convergence, accurate anomaly localization, and robust operation, indicating the effectiveness of the integrated forecasting–dispatch framework for smart industrial grids with high wind penetration. Full article

Accurate vessel track association is a key requirement for maritime traffic monitoring and collision-avoidance systems, yet the Automatic Identification System (AIS) records commonly contain noise, missing intervals, and overlapping trajectories in congested coastal waters. We propose a Dilated Residual Connection Temporal Convolutional Network (DRC-TCN) tailored to AIS sequences; residual dilated blocks with layer normalization enable stable training while capturing long-range temporal dependencies under imperfect data. Beyond kinematic inputs, we augment AIS with buoy-based meteorological variables (wind direction and speed, gust, pressure, air temperature, and sea surface temperature) via time-aligned nearest-station fusion, allowing the model to account for environmental effects on vessel motion. Experiments on New York coastal AIS data show that DRC-TCN outperforms CNN-LSTM and vanilla TCN baselines, improving F1 score by up to 99.3% and achieving 99.7% accuracy. The results indicate that environment-aware temporal modeling strengthens the robustness of track association and supports situational awareness for next-generation intelligent navigation and ocean engineering applications. Full article