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.
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.
CNNs improve wind speed forecasts in the Netherlands.
problem Limited spatial patterns in current post-processing methods.
method Convolutional Neural Networks (CNNs) for spatial wind speed information.
result CNNs produce better probabilistic forecasts with higher Brier skill scores.
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.
New method combines model forecasts and real-time observations for hourly wind speed predictions.
problem Filling the six-hour gap between weather model runs for accurate hourly wind speed forecasts.
method Combines quasi-real-time observed wind speed and weather model predictions using a novel Ensemble Model Output Statistics (EMOS) strategy.
result Successfully improved wind speed predictions compared to observed data from SYNOP stations.
This paper combines and improves probabilistic forecasts of wind speeds using advanced statistical methods.
problem Improving accuracy and reliability of probabilistic forecasts in wind speed prediction.
method Adapting prediction with expert advice theory to probabilistic forecasts, combining raw or post-processed ensembles, and using CRPS and Jolliffe-Primo tests.
result Combining probabilistic forecasts can lead to more reliable and skillful predictions, as shown by the Jolliffe-Primo test.
Integrating wind power into the grid is challenging because of its random nature. Integration is facilitated with accurate short-term forecasts of wind power. The paper presents a spatio-temporal wind speed forecasting algorithm that incorporates the time series data of a target station and data of surrounding stations…
New models improve stock and wind speed forecasting.
problem Lack of posterior distribution in stochastic volatility models.
method Re-cast stochastic volatility models as hierarchical Gaussian processes with specialized covariance functions.
result Volt and Magpie models significantly outperform baselines in forecasting.
Improves forecast calibration for extreme events using modified loss functions.
problem Improperly specified models do not issue calibrated forecasts for extreme events.
method Adapting loss functions based on weighted scoring rules and tail miscalibration regularization.
result Calibrated forecasts for extreme wind speeds can be improved by suitable adaptations to the loss function during model training.
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.
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.
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.
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.
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.
Wind speed prediction improved using a novel deep ensemble learning model inspired by jet aerodynamics.
problem Accurate wind speed forecasting for renewable energy production.
method Proposes a novel Deep Ensemble Learning using Jet-like Architecture (DEL-Jet) to enhance robustness and generalization of a learning system.
result The DEL-Jet technique improves the robustness and generalization of the learning system, as shown by performance evaluations.
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.
Deep learning models predict solar irradiance for short-term forecasts.
problem Accurate prediction of solar irradiance for renewable energy integration.
method Sequence-to-sequence LSTM models for GHI forecasting, incorporating spatial-temporal features.
result LSTM models outperform traditional techniques in short-term GHI forecasting.
Paper presents novel online MTL methods using WRLS and OSLSSVR.
problem Online Multi-Task Learning (MTL) Regression Problems
method Develops recursive versions of WRLS and OSLSSVR for MTL.
result Achieves exact and approximate recursions with quadratic cost.
Efficiently combines probabilistic predictions using kernel embeddings.
problem Combining multiple probabilistic predictions to improve forecast accuracy.
method Embed predictions into RKHS, optimizes kernel-based scoring rules, finds efficient implementation.
result Generalised linear pool outperforms traditional linear pool in operational wind speed forecasts.
We consider multi-task regression models where the observations are assumed to be a linear combination of several latent node functions and weight functions, which are both drawn from Gaussian process priors. Driven by the problem of developing scalable methods for forecasting distributed solar and other renewable powe…
In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park. We combine a multivariate seasonal time varying threshold autoregressive moving average (TVARMA) model with a power threshold generalized autoregressive conditional heteroscedastic (power-TGARCH) model. The mo…
In this paper, we applied the multifractal detrended fluctuation analysis to the daily means of wind speed measured by 119 weather stations distributed over the territory of Switzerland. The analysis was focused on the inner time fluctuations of wind speed, which could be more linked with the local conditions of the hi…
We focus on wind power modeling using machine learning techniques. We show on real data provided by the wind energy company Ma{ï}a Eolis, that parametric models, even following closely the physical equation relating wind production to wind speed are outperformed by intelligent learning algorithms. In particular, the CA…
A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilber…
Study compares two methods for predicting extreme atmospheric events.
problem Forecasting threshold exceedances of atmospheric variables like temperature and wind speed.
method Direct vs. full distribution probabilistic methods for rare events.
result Full distribution approach outperforms direct method for extreme events.
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.
Using artificial neural network for the prediction of heat demand has attracted more and more attention. Weather conditions, such as ambient temperature, wind speed and direct solar irradiance, have been identified as key input parameters. In order to further improve the model accuracy, it is of great importance to und…
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.
Framework for renewable energy forecasting and feature engineering.
problem Forecasting and feature extraction for multivariate processes in renewable energy.
method Derivative-free optimization, ensemble of sequence-to-sequence networks, additive resampling, Bootstrap aggregating.
result The proposed method outperforms other machine learning techniques in long-term forecasts and feature selection.
Improved visibility forecasts using statistical post-processing.
problem Accurate and reliable predictions of visibility are crucial in aviation and transportation.
method Calibrated ensemble forecasts using locally, semi-locally, and regionally trained POLR and MLP classifiers.
result Post-processing improves forecast skill and POLR models outperform MLPs.
Textual data predicts electricity consumption and weather.
problem Lack of textual data in time series prediction models.
method Used TF-IDF and neural word embeddings to predict time series from text.
result Textual data can predict time series with sufficient accuracy.
Researchers use Gaussian Process Regression to improve accuracy of a low-cost hot-wire anemometer.
problem Improving accuracy of low-cost hot-wire anemometers in varying temperatures.
method Probabilistic calibration using Gaussian Process Regression.
result The method provides good performance in estimating actual wind speeds, including uncertainty.
New method improves wind and solar energy forecasts by 48 hours.
problem Volatility of wind and solar energy makes accurate forecasts difficult.
method Two-step machine learning approach to calibrate ensemble forecasts.
result Statistical post-processing improves forecast skill by at least 48 hours.
New neural network captures spatial correlations in wind speed predictions.
problem Uncertainty quantification in neural network predictions for high-dimensional, correlated data.
method Training neural networks with multidimensional Gaussian loss, preserving spatial correlation and computational tractability.
result Demonstrated super-resolution of surface wind speed with explicit correlation modeling.
We propose a novel method to forecast the future from the present using time-reversed data.
problem Forecasting the future from past data, exploiting temporal asymmetry.
method Retrodictive forecasting via inverse MAP optimization over a Conditional Variational Autoencoder (CVAE).
result The method successfully predicts future events in time-reversible and irreversible processes.
A meticulous assessment of the risk of impacts associated with extreme wind events is of great necessity for populations, civil authorities as well as the insurance industry. Using the concept of spatial risk measure and related set of axioms introduced by Koch (2017, 2019), we quantify the risk of losses due to extrem…
Modeling wind dynamics in Saudi Arabia using deep learning and stochastic PDEs.
problem Accurately modeling spatio-temporal wind patterns in a large, diverse, and understudied region.
method Energy distance-based spatial reduction, sparse stochastic Echo State Network, non-stationary stochastic PDE reconstruction.
result Produces more accurate wind speed and energy forecasts, saving $1 million annually.
GenFormer uses deep learning to generate complex stochastic data.
problem Creating synthetic stochastic data that matches real-world statistical properties.
method Transformer-based deep learning model that maps Markov state sequences to time series values.
result GenFormer preserves target marginal distributions and other statistical properties in multivariate spatio-temporal data.
LADaR framework calibrates machine learning models for instance-wise predictions.
problem Challenges in assessing and calibrating predictive distributions for complex inputs.
method Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR) framework and extttCal−PIT algorithm. result Achieves better instance-wise calibration than existing methods in galaxy distance estimation.
In this study, the wind data series from five locations in Aegean Sea islands, the most active `hotspots' in terms of refugee influx during the Oct/2015 - Jan/2016 period, are investigated. The analysis of the three-per-site data series includes standard statistical analysis and parametric distributions, auto-correlati…
Hurricanes are cyclones circulating about a defined center whose closed wind speeds exceed 75 mph originating over tropical and subtropical waters. At landfall, hurricanes can result in severe disasters. The accuracy of predicting their trajectory paths is critical to reduce economic loss and save human lives. Given th…
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.
New statistical methods for analyzing distributions using Wasserstein metric.
problem Statistical analysis of probability distributions on the real line.
method Projected methods exploiting Wasserstein metric and Riemannian structure.
result Projected PCA and regression methods are faster and more flexible.
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.
The paper introduces novel Gaussian process models for vector-valued signals on manifolds.
problem Modeling vector-valued signals on non-Euclidean domains, especially for applications like wind speeds.
method Intrinsically defined Gaussian vector fields on manifolds, accounting for manifold geometry.
result Gaussian vector fields provide more refined inductive biases than extrinsic fields.
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.
Eigenvalue estimates without Bakry-Emery-Ricci bounds established.
problem Eigenvalue estimates for Laplace-Beltrami operators with drift.
method Establishes a lower bound for real eigenvalues without assuming self-adjointness or additional regularity of the drift.
result Establishes a lower bound on the spectrum of Laplace-Beltrami operators without assuming a lower bound for the Bakry-Emery Ricci tensor.
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.