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.
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.
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.
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.
Wind energy forecasting helps to manage power production, and hence, reduces energy cost. Deep Neural Networks (DNN) mimics hierarchical learning in the human brain and thus possesses hierarchical, distributed, and multi-task learning capabilities. Based on aforementioned characteristics, we report Deep Belief Network …
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.
Wind power prediction is of vital importance in wind power utilization. There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction. In th…
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.
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.
Improved LSTM models predict wind power more accurately with weather data.
problem Poor performance of generic LSTM models on wind power data.
method Contextualized LSTM models using weather forecast data and modifications to address model shortcomings.
result Increased accuracy and reduced naive character in predictions.
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.
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.
Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in …
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.
Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid for better integrating renewable energy sources such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form…
Paper tackles temporal overfitting in wind power curve modeling.
problem Temporal overfitting in wind power curve modeling.
method Proposes a Gaussian process-based method to partition and model time-invariant and time-varying components.
result Significant improvement in predicting responses for different time periods.
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.
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.
Study builds dataset and benchmarks ML models for accurate solar and wind power forecasting in France.
problem Accurate prediction of non-dispatchable renewable energy sources for grid stability and price prediction.
method Comprehensive methodology using machine learning models trained with spatially explicit weather data and production site capacity.
result Neural networks outperform traditional models in forecasting solar and wind power production in France.
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.
Enhances renewable energy forecasts and trading profits.
problem Improving accuracy and economic benefits of renewable energy forecasts and trading.
method Data-driven newsvendor model using features from multiple sources.
result Significant reduction in balancing costs and improvement in forecast accuracy.
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…
Extends neural diffusion processes for multi-task regression.
problem Limited to single-task inference, existing formulations cannot capture dependencies across related tasks.
method Introduces a task encoder to condition diffusion model on low-dimensional representations of context observations.
result Improves predictive performance and uncertainty calibration across related functions.
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…
Extended CSGE improves power and cyclist movement forecasting.
problem Power and cyclist movement forecasting challenges.
method Extended Coopetitive Soft Gating Ensemble (XCSGE) with flexible weighting.
result Improves prediction performance by up to 30% for solar power forecasting.
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…
Paper optimizes internal balancing of wind and hydropower to reduce intraday market volatility.
problem Reduction of intraday market volatility for power producers with wind and hydropower assets.
method Internal balancing within the same river system and sales/purchase in a pay-as-bid intraday market.
result Reduction in short-term marginal cost and risk through internal balancing.
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…
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.
This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamic filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependen…
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.
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.
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…
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.
PhI-GPR improves power grid state estimation and forecasting.
problem Accurate state estimation and forecasting in power grids with sparse measurements.
method Physics-informed Gaussian process regression (PhI-GPR) for stochastic differential equations.
result PhI-GPR provides more accurate forecasts and estimates of power grid states compared to ARIMA.
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.
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.
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.
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.
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…
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.
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.
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.
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 develops robust OPF method using contextual information.
problem Optimal Power Flow problem under incomplete uncertainty knowledge.
method Distributionally robust chance-constrained formulation with probability trimmings and optimal transport.
result Distributional robustness improves expected cost and system reliability.
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.
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.
Active learning reduces smart meter data needs for better electric load predictions.
problem Inaccurate and costly electric load predictions due to insufficient data.
method Active learning to collect more informative data subsets.
result Electric load predictions can be made with about half the data using active learning.