No bi-Lipschitz homeomorphism can unwind spirals with sub-exponential winding radii.
problem Unwinding spirals with sub-exponential winding radii.
method Analyzing bi-Lipschitz homeomorphisms of R^2.
result No bi-Lipschitz homeomorphism exists for spirals with sub-exponential winding radii.
We show, using a theorem of Milnor and Margulis, that string theory on compact negatively curved spaces grows new effective dimensions as the space shrinks, generalizing and contextualizing the results in hep-th/0510044. Milnor's theorem relates negative sectional curvature on a compact Riemannian manifold to exponenti…
Proves a generalized isoperimetric inequality for spheres in dimensions 4 and above.
problem Proving a generalized isoperimetric inequality for spheres in dimensions 4 and above.
method Reduced to a theorem about thick embeddings of graphs, proved using Kolmogorov-Barzdin theorem and max-flow min-cut theorem. Counterexample in dimension 3 uses coarea inequality and winding number computation.
result A generalized isoperimetric inequality for spheres in dimensions 4 and above.
Paper proposes a method to identify wind hazard types and predict extreme wind speeds.
problem Difficulty in identifying wind hazard types from meteorological data records.
method Numerical pattern recognition method with feature extraction and generalization.
result Algorithm performance validated using K-fold cross-validation and real-world data.
The paper develops a neural network to predict wind speed from visual observations.
problem Accurate wind speed prediction for renewable energy and weather forecasting.
method Coupled Convolutional and Recurrent Neural Network architecture trained on visual flow-structure interactions.
result The model accurately predicts wind speeds with low error compared to cup anemometer measurements.
DeepMIDE forecasts wind speeds across space, time, and height for offshore wind energy.
problem Forecasting wind speeds across multiple heights for large offshore wind turbines.
method Statistical deep learning model that jointly models wind speeds at different heights using a multi-output integro-difference equation.
result DeepMIDE forecasts outperform traditional methods in real-world offshore wind energy data.
Study short-term wind power and speed predictions using machine learning.
problem Accurate short-term wind power and speed predictions for energy systems.
method Combining numerical weather prediction models with local observations, using machine learning for variable selection and forecasting.
result Improved wind power and speed predictions for 4-hour ahead using machine learning.
A new model predicts wind speed using multiple meteorological variables.
problem Precise wind speed forecasting for wind power producers and grid operators.
method Multi-variable Stacked Long Short-Term Memory (LSTM) network.
result The proposed MSLSTM model outperforms traditional methods in wind speed prediction.
WindDragon forecasts wind power with deep learning.
problem Accurate short-term wind power forecasting is crucial for grid operation.
method Automated Deep Learning combined with Numerical Weather Predictions.
result Automated Deep Learning improves wind power forecasting accuracy.
Winding numbers help classify curves on special surfaces.
problem Classifying regular closed curves on aspherical surfaces.
method Defined winding numbers and proved homotopy equivalence.
result Two curves are regularly homotopic if and only if they have the same winding number and are freely homotopic.
WaveletAE detects wind turbine blade icing using SCADA data.
problem Detecting blade icing in wind turbines for maintenance.
method Wavelet-enhanced autoencoder with CNN-LSTM architecture.
result WaveletAE outperforms other anomaly detection methods.
New method calculates winding of geodesics on surfaces.
problem Understanding the distribution of geodesics on surfaces.
method Introducing a new construction of winding numbers for geodesics on cusped hyperbolic orbifolds.
result Winding numbers can be expressed by Rademacher symbols for various arithmetic families of surfaces.
Machine learning outperforms traditional models in wind power forecasting.
problem Improving wind power prediction accuracy using machine learning.
method Used CART-Bagging algorithm and refined predictor selection methods.
result Machine learning algorithms, especially CART-Bagging, provide stable and promising wind power forecasts.
Study on wind-tree models yields formulas for periodic trajectories.
problem Counting periodic trajectories in wind-tree models.
method Asymptotic formulas and explicit computation of Siegel-Veech constants.
result Asymptotic formulas for closed billiard trajectories in wind-tree models.
Wind turbine status classification models are improved for cross-site applicability.
problem Standard models don't generalize across different wind turbines.
method Data normalization and convolutional neural networks with feature-space extension.
result Trained models successfully classified wind turbines from different sites.
Proves large cobordism genus for winding number patterns.
problem Proving large cobordism genus for winding number patterns.
method Using knots and cobordism theory to show existence of large genus.
result Minimal genus of cobordism between winding number patterns can be arbitrarily large.
Improved wind speed forecasts for power generation using machine learning.
problem Improving the accuracy and reliability of wind speed predictions for power generation.
method A novel machine learning approach for calibrating wind speed ensemble forecasts.
result The proposed method improves the calibration and accuracy of probabilistic and point forecasts.
DA improves solar wind forecasts by updating model boundary conditions.
problem Improving solar wind forecasting accuracy.
method Variational Data Assimilation with solar wind model and in-situ observations.
result DA forecasts are more accurate than non-DA forecasts, especially when STEREO-B's latitude is offset from Earth.
Study on wind speed variability in Switzerland using multifractal analysis.
problem Understanding wind speed variability in mountainous regions.
method Multifractal detrended fluctuation analysis applied to daily wind speed data.
result Persistent and multifractal nature of wind speed series, with larger fluctuations in Swiss plateau.
Machine learning predicts wind pressures around circular cylinders efficiently.
problem Predicting wind pressures around circular cylinders using traditional methods is costly and time-consuming.
method Trained GBRT models using Reynolds number, turbulence intensity, and circumferential angle as inputs.
result GBRT models accurately predict wind pressures for a wide range of Reynolds and turbulence intensities.
Deep CNN predicts wind power with 49.83% less error.
problem Wind power prediction lacks temporal and spatial representation.
method Mapped turbine data to state maps, applied deep CNN.
result Deep CNN reduces MSE by 49.83%.
Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.
problem Optimizing wind farm layout due to limited space and conflicting objectives.
method Set-based multi-objective Bayesian optimisation using Gaussian process.
result Demonstrates potential of set-based Bayesian multi-objective optimisation for wind farm layout.
Study predicts wind energy potential in Gulf of Oman using climate models.
problem Predicting future wind energy potential in the Gulf of Oman.
method Used ERA5 and MENA simulations to project historical and future wind energy variability.
result Selected locations have suitable potential for wind power turbine construction.
Method reconstructs missing wind farm data using graph theory and nearest neighbors.
problem Missing data in wind farm records due to sensor failures.
method Combines spectral graph theory and k-Nearest Neighbors to estimate missing data.
result Significant improvement in data reconstruction over existing methods.
New method predicts wind farm power and wakes using weather patterns.
problem Inefficient and computationally intensive wind energy resource assessment.
method Unsupervised clustering of ERA5 data on wind velocity, WRF simulations at cluster centers, and post-processing.
result Accurate long-term predictions of power and wakes with reduced computational time.
Study uses ANFIS to assess wind power under climate change.
problem Tackles climate change impact on wind power potential.
method Employed ANFIS to match climate model data with reference data.
result Real wind power potential lower than projected.
Bayesian method models multivalued power data from wind farms.
problem Accurate modeling of power curves with multivalued relationships.
method Overlapping mixture of probabilistic regression models.
result Model accurately represents practical power data.
DBN-WP predicts wind power with low RMSE, MAE, and SDE.
problem Wind energy forecasting for cost reduction.
method Deep Belief Network (DBN) with multiple RBM layers for feature generation and regression.
result DBN-WP achieves low RMSE, MAE, and SDE values.
Generative model improves wind field downscaling from coarse climate models.
problem Limited spatial resolution and biases in GCMs for wind energy studies.
method SerpentFlow for domain alignment and conditional fine-scale learning.
result Improved spatial coherence, inter-variable consistency, robustness under climate change.
Adaptive Transfer Learning improves wind power prediction accuracy.
problem Efficiently predicting wind power using limited labeled data.
method Adaptive Transfer Learning in Deep Neural Networks for wind power prediction.
result Adaptive Transfer Learning improves prediction accuracy across different wind farms and task domains.
New grading on algebras of curves by winding number.
problem Understanding the structure of algebras of curves.
method Constructing a new grading on the Goldman Lie algebra and related algebras by winding number.
result Induces a new grading on the HOMFLY-PT skein algebra and related algebras.
Formula for winding numbers on non-null-homotopic curves on surfaces.
problem Determining winding numbers for non-null-homotopic curves on surfaces.
method Generalized a Whitney-type formula for winding numbers of non-null-homotopic curves on aspherical surfaces.
result Formula for winding numbers on non-null-homotopic curves on aspherical surfaces.
Machine learning analyzes AIS data to track offshore wind turbine installations.
problem Tracking and optimizing the installation of offshore wind turbines using jack-up vessels.
method Applied machine learning to AIS data from jack-up vessels to identify turbine locations and installation times.
result Automated identification of turbine locations and installation times using only AIS data.
We introduce a measure for estimating the best risk-return relation of power production in wind farms within a given time-lag, conditioned to the velocity field. The velocity field is represented by a scalar that weighs the influence of the velocity at each wind turbine at present and previous time-steps for the presen…
MLM models match or exceed RN in generating wind power time series without location info.
problem Generating accurate long-term wind power time series without location information.
method Applied neural networks to MERRA2 wind speed data with and without location info.
result MLM models produce time series of equal or better quality than RN.
Paper proposes KSHMM for short-term wind-speed forecasting.
problem Short-term wind-speed prediction challenge.
method Kernel Spectral Hidden Markov Model (KSHMM) for time series forecasting.
result KSHMM-based technique offers comparable or better performance than other methods.
Wind energy producer optimizes trading policies using updated forecasts.
problem Maximizing profit from wind energy sales in various markets.
method Stochastic model for forecast evolution, dynamic trading policies.
result Quantifies expected future gain and forecasts' economic value.
Case study shows impact of co-optimizing energy and reserve for wind energy.
problem Impact of lack of co-optimization of energy and reserve in high wind penetration scenarios.
method Developed two models with and without co-optimization, calibrated with Spanish market parameters.
result Models show significant differences in energy and reserve management.
CESAR improves wind speed and power forecasting for high-resolution simulations.
problem Accurate high-resolution wind forecasting for efficient power grid management.
method A spatio-temporal neural network model using deep convolutional autoencoder and echo state network.
result CESAR provides up to 17% improvement in wind speed and power forecasting compared to best alternatives.
Study uses CNNs to upscale wind speed data from 100 km to 3 km, improving subgrid-scale variability.
problem Recovering fine-scale wind speed information from coarse data.
method Convolutional neural networks (CNNs) with different input configurations (coarse wind speed, fine-scale topography, diurnal cycle) were tested.
result CNN models with coarse wind and fine topography inputs perform best in generalizing to unseen regions.
Flexible GP model improves wind power prediction accuracy.
problem Accurate probabilistic prediction of wind power for grid stability.
method Heteroscedastic non-stationary Gaussian process with generalised spectral mixture kernel.
result The proposed model outperforms conventional GP models in wind power prediction.
Novel framework improves wind power forecasts by bundling assets and using machine learning.
problem Inaccurate forecasts of intermittent renewable generation, especially wind power.
method Bundle-Predict-Reconcile (BPR) framework integrating asset bundling, machine learning, and forecast reconciliation.
result Significant improvement in forecast accuracy, especially at the fleet level.
Study examines economic impact of wind energy on Colombian electricity market.
problem Impact of wind energy on Colombian electricity market pricing and conventional generation.
method Built a unit commitment model to simulate market legislation and system data.
result Wind energy reduces the operation of gas-fired plants by up to 20%.
Study quantifies risk of extreme wind events using spatial risk measures.
problem Assessing risk of impacts from extreme wind events.
method Spatial risk measure axioms, Brown-Resnick max-stable random fields, powers of max-stable random fields.
result Spatial risk measures associated with extreme wind speeds satisfy risk measure axioms.
The article presents a fast wind power forecasting model combining time series models.
problem Accurate short- to medium-term wind power forecasting for individual wind turbines.
method Combines multivariate TVARMA and power-TGARCH models with shrinkage techniques.
result The approach provides accurate forecasts of wind power up to 48 hours ahead.
Hybrid model improves wind speed prediction accuracy using MLP and WOA.
problem Improving wind speed prediction accuracy for renewable energy control.
method Combining MLP with Whale Optimization Algorithm (WOA) for data preprocessing and model optimization.
result The hybrid MLP-WOA model outperformed standalone MLP model in wind speed prediction accuracy.
Optimal wind farm placement using quantile constraints for better power output.
problem Optimizing wind farm placement to maximize power output considering spatial and temporal wind speed correlations.
method Used a probabilistic neural network with ReLU activation functions to reformulate constraints as linear ones, embedding them into a two-stage stochastic optimization problem.
result The constraint learning approach outperforms classical methods, especially for risk-averse investors.
Wind Riemannian structures generalize Randers metrics and are classified for constant flag curvature.
problem Classifying wind Riemannian structures of constant flag curvature.
method Using the natural data for Zermelo navigation problem, constructing WRS from a Riemannian metric and a vector field (wind).
result Local and global classification of wind Riemannian structures of constant flag curvature.