Wind Power Prediction Research Articles

Enhanced PV power prediction using LSTM-integrated soft actor–critic model based on long short-term memory

Accurate PV power prediction is crucial in efficiently operating intelligent power grid systems. Data-driven approaches have shown high performance in predictive tasks. Deep reinforcement learning (DRL) merges deep learning with reinforcement learning and has been widely studied for optimization challenges in various fields. However, limited research has focused on applying DRL to ultra-short-term PV power prediction. Hence, a soft actor–critic (SAC) model using long short-term memory (LSTM) is proposed for predicting PV power. To accomplish this, first, the PV power problem is modeled as a Markov decision process with historical weather data and PV power data as state inputs. Then, LSTM is integrated into the critic network of SAC to enhance its memory capability, thus improving prediction accuracy. Ultimately, the agent engages with the environment to address the optimization problem. Experimental results indicate that the proposed model attains greater prediction accuracy. This study explores the potential of DRL for PV power prediction, and the proposed method can be extended to other prediction fields, including grid prediction and wind power prediction.

Short-term wind power prediction based on adaptive Stacking integrated learning model

Abstract Aiming at the problem that when a single combined model uses multiple features for short-term wind power prediction with a small number of training samples, it is difficult to capture the characteristics of time series data, resulting in a large generalization error of the prediction model. This paper proposes a short-term wind power prediction method based on an adaptive Stacking integrated learning model. This ensemble prediction method selects four basic models with different working principles, namely Random Forest (RF), Transformer, Bidirectional Long Short-Term Memory Network (BiLSTM), and Gated Temporal Convolutional Network (GTCN). Since the meta-learner of Stacking ensemble learning cannot fuse each basic model differentially, this limits the advantage of the fusion of model prediction results to a certain extent, leading to the accumulation of errors. In this paper, by introducing an adaptive dynamic attention mechanism, weights are assigned to the preliminary prediction results of each basic model to form weighted input features. Finally, the weighted input features of each basic model are sent to the meta-learner for ensemble training, and the prediction results are mapped and fused to obtain the final wind power prediction result. According to the actual power generation data of a certain offshore wind farm in Fujian Province, taking the data in December as an example, the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient ( ) of the proposed model are 97.13, 65.77, and 0.91 respectively. The comparison results with multiple models show that the proposed method has higher prediction accuracy.

Meta Model Approach for Real Time and Short-Term Forecasting Of Wind Turbine Power Generation With Interactive 24-Hour Dashboard

Abstract This study evaluates the performance of various machine learning algorithms (Linear Regression, SVR, AdaBoost, XGBoost, Gradient Boosting, Decision Tree, Random Forest, Extra Trees, CatBoost) for predicting wind power generation. We investigate their strengths and weaknesses through extensive experimentation using data from Kaggle and ENTSO-E.To enhance accuracy, we employ a meta-model approach and incorporate data cleaning techniques. We integrate statistical methods, artificial neural networks, and deep learning for improved short-term forecasting. A key outcome is the development of a real-time GUI dashboard that utilizes the OpenWeather API to fetch wind data and display predictions. This user-friendly interface features visualizations, alerts, and real-time data updates.Our results demonstrate that the selected meta-model significantly surpasses traditional methods, achieving superior metrics like R-squared and RMSE. This research showcases the potential of hyperparameter-tuned machine learning for precise wind power prediction, contributing to increased renewable energy utilization and reduced greenhouse gas emissions.

Short-term wind power prediction with a new PCC-GWO-VMD and BiGRU hybrid model enhanced by attention mechanism

The integration of wind power into the grid significantly depends on the accuracy and reliability of wind power prediction. However, the task of wind power prediction faces significant challenges due to the stochastic nature of wind speed. This study proposes a novel deep hybrid short-term wind power prediction model: PCC-GWO-VMD-BiGRU-Attention. The model integrates Pearson correlation coefficient (PCC), variational mode decomposition (VMD), gray wolf optimization (GWO), and attention mechanism optimized bidirectional gated recurrent unit (BiGRU-Attention) to enhance prediction accuracy and robustness, while quantifying prediction uncertainty through deep ensemble methods. First, PCC is used to identify the key factors affecting wind power, thereby improving the model's computational performance. Second, GWO can dynamically adjust the key parameters [K, α] in VMD for optimal decomposition of the input time series, thereby improving prediction accuracy. Finally, the BiGRU-Attention model is utilized to extract global temporal features in historical sequences, while the attention mechanism focuses on key information in the sequences to further enhance prediction performance. Experimental results show that, compared to other deep learning models, this model achieves the highest accuracy in short-term wind power prediction with an root mean square error of 0.2677 Megawatt (MW), mean absolute error of 0.1509 Megawatt (MW), mean square error of 0.0717 Megawatt (MW), and coefficient of determination (R2) of 0.9605. This method has significant practical value and contributes to ensuring safe operation of wind farms.

Ultra-short-term wind power prediction based on hybrid denoising with improved CEEMD decomposition

Ultra-short-term wind power prediction based on hybrid denoising with improved CEEMD decomposition

Data-driven deep learning model for short-term wind power prediction assisted with WGAN-GP data preprocessing

Data-driven deep learning model for short-term wind power prediction assisted with WGAN-GP data preprocessing

Wind power prediction based on improved self-attention mechanism combined with Bi-directional Temporal Convolutional Network

Wind power prediction based on improved self-attention mechanism combined with Bi-directional Temporal Convolutional Network

Few-shot wind power prediction using sample transfer and imbalanced evolved neural network

Few-shot wind power prediction using sample transfer and imbalanced evolved neural network

Data-driven wind power prediction model based on improved generative adversarial network

Abstract Accurate and timely wind power forecast is difficult because of the volatility and intermittency of wind energy, which are strongly influenced by meteorological conditions. In order to solve this problem, this research suggests a data-driven wind power prediction technique based on Elman neural networks and an enhanced generative adversarial network (GAN). First, outliers in the supervisory control and data acquisition (SCADA) power and wind speed data are found by combining the methods of density clustering and regression threshold truncation. To guarantee the temporal continuity of wind power data with the original characteristics, minority class samples are then generated using an enhanced GAN and added to the dataset. In order to obtain an accurate wind power prediction, the balanced data is then fed into a prediction model that combines the ABC algorithm and Elman neural networks. The fitness value is the mean squared error (MSE) of the training data, and the optimization objective is the connection weights of the Elman neural network. Results from experiments show that the suggested model outperforms earlier state-of-the-art techniques and significantly increases wind power prediction accuracy. It also has the advantages of high stability and quick convergence speed, and it can capture the long-term dependencies of wind power data.

Wind power prediction using stacking and transfer learning

As countries focus more on renewable energy, especially wind power, predicting wind power output accurately is crucial for managing power grids and saving costs. This paper presents a new method for ultra-short-term wind power prediction using a combination of Stacking and Transfer Learning. To improve accuracy, we first reduce the data dimensions using PCA. Then, we use several models like LSTM, BiLSTM, GRU, BiGRU, and LSTM-Attention as base learners. These models are combined using a Stacking ensemble model. We also use Transfer Learning to share trained models between tasks, which helps improve performance. Tests with real data from a wind farm show that our method is more accurate than single models.

Adaptive expert fusion model for online wind power prediction.

Adaptive expert fusion model for online wind power prediction.

Performance analysis of wind-hydrogen energy storage system using composite objective optimization proactive scheduling strategy coordinated with wind power prediction

Performance analysis of wind-hydrogen energy storage system using composite objective optimization proactive scheduling strategy coordinated with wind power prediction

Probabilistic prediction of wind power based on QRNN and kernel density estimation

Probabilistic prediction of wind power based on QRNN and kernel density estimation

Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction.

Adaptive singular spectral decomposition hybrid framework with quadratic error correction for wind power prediction.

CL-TGD: A novel point-wise contrastive learning with dynamic temporal granularity data incorporation for wind power prediction

CL-TGD: A novel point-wise contrastive learning with dynamic temporal granularity data incorporation for wind power prediction

A comprehensive wind power prediction system based on correct multiscale clustering ensemble, similarity matching, and improved whale optimization algorithm—A case study in China

A comprehensive wind power prediction system based on correct multiscale clustering ensemble, similarity matching, and improved whale optimization algorithm—A case study in China

Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes

Synergistic Artificial Intelligence framework for robust multivariate medium-term wind power prediction with uncertainty envelopes

A Review of Wind Power Prediction Methods Based on Multi-Time Scales

In response to the ‘zero carbon’ goal, the development of renewable energy has become a global consensus. Among the array of renewable energy sources, wind energy is distinguished by its considerable installed capacity on a global scale. Accurate wind power prediction provides a fundamental basis for power grid dispatching, unit combination operation, and wind farm operation and maintenance. This study establishes a framework to bridge theoretical innovations with practical implementation challenges in wind power prediction. This work uses a narrative method to synthesize and discuss wind power prediction methods. Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. The analysis reveals that the data-driven prediction model dominates ultra-short-term predictions through rapid response to volatility, while the hybrid method enhances short-term precision. Mid-term predictions increasingly integrate climate dynamics to address seasonal variability. A key contribution lies in unifying fragmented methodologies into a decision support framework that prioritizes the time scale, model adaptability, and spatial constraints. This work enables practitioners to systematically select optimal strategies and advance the development of forecasting systems that are critical for highly renewable energy systems.

A Multi-Strategy Artificial Electric Field Algorithm for Numerical Optimization

Artificial electric field algorithm (AEFA) is a metaheuristic optimization algorithm proposed in recent years, which has been successfully applied to address various optimization problems. However, it is likely to converge prematurely or fall into local optima when solving complex problems. To overcome these disadvantages, a multi-strategy artificial electric field algorithm (MAEFA) is proposed in this paper. For the MAEFA algorithm, the global optimal solution information is utilized to improve the diversity of population and global search ability. Then, the adaptive Coulomb’s constant is configured to balance the global exploration and local search. Also, a restart strategy is designed to further alleviate the premature convergence. To validate the effectiveness of MAEFA, it is compared with three AEFA algorithms and several other evolutionary algorithms on 14 test problems presented in CEC 2005 and 13 basic benchmark functions. Furthermore, a wind power prediction model based on MAEFA algorithm and back-propagation (BP) neural network is established to investigate its application ability. Experiments show that MAEFA is significantly superior to other algorithms in tackling these benchmark functions with different dimensions. Furthermore, in terms of wind power prediction, the BP neural network model optimized by MAEFA algorithm also provides higher prediction accuracy.

Wind Power Prediction Method and Outlook in Microtopographic Microclimate

With the increase in installed capacity of wind turbines, the stable operation of the power system has been affected. Accurate prediction of wind power is an important condition to ensure the healthy development of the wind power industry and the safe operation of the power grid. This paper first introduces the current status of wind power prediction methods under normal weather, and introduces them in detail from three aspects: physical model method, statistical prediction method and combined prediction method. Then, from the perspectives of numerical simulation analysis and statistical prediction methods, the wind power prediction method under icy conditions is introduced, and the problems faced by the existing methods are pointed out. Then, the accurate prediction of wind power under icing weather is considered, and two possible research directions for wind power prediction under icy weather are proposed: a statistical prediction method for classifying and clustering wind turbines according to microtopography, combining large-scale meteorological parameters with small-scale meteorological parameter correlation models and using machine learning for cluster power prediction, and a power prediction model converted from the power prediction model during normal operation of the wind turbine to the power prediction model during icing. Finally, the research on wind power prediction under ice-covered weather is summarized, and further research in this area is prospected.