Wind Power Prediction Research Articles

Research on Physically Constrained VMD-CNN-BiLSTM Wind Power Prediction

Accurate forecasting of wind power is crucial for addressing energy demands, promoting sustainable energy practices, and mitigating environmental challenges. In order to improve the prediction accuracy of wind power, a VMD-CNN-BiLSTM hybrid model with physical constraints is proposed in this paper. Initially, the isolation forest algorithm identifies samples that deviate from actual power outputs, and the LightGBM algorithm is used to reconstruct the abnormal samples. Then, leveraging the variational mode decomposition (VMD) approach, the reconstructed data are decomposed into 13 sub-signals. Each sub-signal is trained using a CNN-BiLSTM model, yielding individual prediction results. Finally, the XGBoost algorithm is introduced to add the physical penalty term to the loss function. The predicted value of each sub-signal is taken as the input to get the predicted result of wind power. The hybrid model is applied to the 12 h forecast of a wind farm in Zhangjiakou City, Hebei province. Compared with other hybrid forecasting models, this model has the highest score on five performance indicators and can provide reference for wind farm generation planning, safe grid connection, real-time power dispatching, and practical application of sustainable energy.

A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges

Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios.

Overview of wind power forecasting methods

The integration of large-scale wind power into China's power system poses significant challenges to its operational stability. Therefore, accurate wind power output forecasting is crucial for the smooth operation of the grid. This paper begins by analyzing the key factors influencing wind power generation and classifies different forecasting methods. It then summarizes artificial intelligence-based approaches for wind power prediction. Finally, the paper highlights the current state of wind power forecasting in China and discusses potential future research trends in this field

Wind Power Prediction Based on The SENet-CNN-LSTM Model

aims: To improve the accuracy of wind power prediction background: In recent years, wind power has developed rapidly nationwide and worldwide.How to improve the prediction precision and accuracy of wind power has become a research hotspot in this field objective: This paper aims to construct a wind power forecasting system based on SENet-CNN-LSTM to improve the accuracy and stability of wind power forecasting method: Firstly, the isolated forest algorithm is used to detect abnormal values in wind power historical data, and the linear interpolation method is used to fill in the missing data. Secondly, CNN is used to extract the spatio-temporal characteristics of wind power data, SENet is used to assign different weights to the extracted feature information, and LSTM’s unique gating mechanism is used to memorize and forget the information. Finally, taking the measured historical data of a wind power farm as the sample, six algorithms including CNN, LSTM, CNN-LSTM, SENet-CNN, SENet-LSTM and SENet-CNN-LSTM are used to predict the example result: The prediction results show that the RMSE, MAE and MAPE of SENet-CNN-LSTM are significantly reduced compared with the other five prediction models conclusion: The wind power prediction model based on SENet-CNN-LSTM has high prediction accuracy and good performance other: There is no.

Study of Wind Power Prediction in ELM Based on Improved SSA

This paper proposes a short‐term wind power prediction model based on the improved Sparrow Search Algorithm (SSA) and Extreme Learning Machine(ELM) for anomalous wind power information from wind farms. The objective is to enhance the accuracy of short‐term wind power prediction. The model employs the extraction of features utilizing raw wind power history data from wind farms, in conjunction with the application of Variable Importance in Projection indices in Partial Least Squares (PLS‐VIP). As the ELM network model is susceptible to the influence of randomly generated input weights and thresholds at the outset of training, a solution is proposed whereby the input weights and thresholds of the ELM are optimized using SSA. The optimal weights and thresholds identified by SSA are then applied to the ELM model, thus forming the SSA‐ELM model. To address the limitations of traditional SSA, namely its susceptibility to local optimal solutions and poor global search ability, an improved SSA‐ELM algorithm is proposed. The improved SSA‐ELM algorithm introduces chaotic sequences and an exchange learning strategy to the original SSA. The rationale behind incorporating chaotic sequences is to enhance the quality of the initial solution, ensuring a more uniform distribution of sparrow positions and, consequently, a more diverse sparrow population. This, in turn, enables the algorithm to achieve a more effective global search capability through the utilization of the exchange learning strategy. Subsequently, all the data are fed into the SSA‐ELM model for prediction purposes. The simulation results demonstrate that the model exhibits enhanced prediction accuracy and improved practical applicability in wind power prediction. © 2025 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Recurrent attention encoder–decoder network for multi-step interval wind power prediction

Recurrent attention encoder–decoder network for multi-step interval wind power prediction

A deep learning based short-term wind power prediction model with Fourier self-attention mechanism

A deep learning based short-term wind power prediction model with Fourier self-attention mechanism

A Novel Hybrid Machine Learning Framework for Wind Speed Prediction

The growing urgency of environmental challenges and the depletion of fossil fuels have accelerated the search for sustainable and renewable energy sources. Wind energy, for example, is an important source of green electricity. However, using wind power is challenging due to the variability and unpredictability of wind patterns. Consequently, the ability to predict wind power in advance is crucial. The integration of artificial intelligence within the renewable energy sector could provide a viable solution to this challenge. In this study, we investigate the potential of machine learning to improve wind power forecasting by conducting a comparison of three regression models: K-Nearest Neighbor regression, Random Forest regression, and Support Vector regression. These models are combined with a feature selection technique to forecast wind power. Additionally, we propose a novel hybrid approach that combines these machine learning models with Multiple Linear Regression to address the complexities of wind energy forecasting. The performance of the models is evaluated using the R² score, Mean Absolute Error, and Root Mean Squared Error. The dataset for this study was generated from a numerical simulation conducted at a location with a latitude of 22.55° N and a longitude of -14.33° E. The findings demonstrate that the proposed hybrid model outperforms the individual machine learning models in terms of prediction accuracy. This study provides a solid foundation for future research and development in wind energy forecasting.

Research on Low-Voltage Ride-Through and Intelligent Optimization Control of Wind Turbines Based on Hybrid Power Prediction Models

This paper proposes a dual-loop back-to-back converter coordination control scheme with a DC-side voltage as the primary control target, along with a CROW unloading control strategy for low voltage ride-through (LVRT) capability enhancement. The feasibility and effectiveness of the proposed system topology and control strategy are verified through MATLAB/Simulink simulations. Furthermore, a hybrid short-term wind power prediction model based on data-driven and deep learning techniques (CEEMDAN-CNN-Transformer-XGBoost) is introduced in the wind turbine control system. The coordination control strategy seamlessly integrates wind power prediction, pitch angle adjustment, and the control system, embodying a predictive-driven intelligent optimization control approach. This method significantly improves prediction accuracy and stability, theoretically reduces unnecessary pitch angle adjustments, lowers mechanical stress, and enhances system adaptability in complex operating conditions. The research findings provide a valuable theoretical foundation and technical reference for the intelligent and efficient operation of wind power generation systems.

Data Decomposition Modeling Based on Improved Dung Beetle Optimization Algorithm for Wind Power Prediction

Accurate wind power forecasting is essential for maintaining the stability of a power system and enhancing scheduling efficiency in the power sector. To enhance prediction accuracy, this paper presents a hybrid wind power prediction model that integrates the improved complementary ensemble empirical mode decomposition (ICEEMDAN), the RIME optimization algorithm (RIME), sample entropy (SE), the improved dung beetle optimization (IDBO) algorithm, the bidirectional long short-term memory (BiLSTM) network, and multi-head attention (MHA). In this model, RIME is utilized to improve the parameters of ICEEMDAN, reducing data decomposition complexity and effectively capturing the original data information. The IDBO algorithm is then utilized to improve the hyperparameters of the MHA-BiLSTM model. The proposed RIME-ICEEMDAN-IDBO-MHA-BiLSTM model is contrasted with ten others in ablation experiments to validate its performance. The experimental findings prove that the proposed model achieves MAPE values of 5.2%, 6.3%, 8.3%, and 5.8% across four datasets, confirming its superior predictive performance and higher accuracy.

MIVNDN: Ultra-Short-Term Wind Power Prediction Method with MSDBO-ICEEMDAN-VMD-Nons-DCTransformer Net

Wind speed, wind direction, humidity, temperature, altitude, and other factors affect wind power generation, and the uncertainty and instability of the above factors bring challenges to the regulation and control of wind power generation, which requires flexible management and scheduling strategies. Therefore, it is crucial to improve the accuracy of ultra-short-term wind power prediction. To solve this problem, this paper proposes an ultra-short-term wind power prediction method with MIVNDN. Firstly, the Spearman’s and Kendall’s correlation coefficients are integrated to select the appropriate features. Secondly, the multi-strategy dung beetle optimization algorithm (MSDBO) is used to optimize the parameter combinations in the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, and the optimized decomposition method is used to decompose the historical wind power sequence to obtain a series of intrinsic modal function (IMF) components with different frequency ranges. Then, the high-frequency band IMF components and low-frequency band IMF components are reconstructed using the t-mean test and sample entropy, and the reconstructed high-frequency IMF component is decomposed quadratically using the variational modal decomposition (VMD) to obtain a new set of IMF components. Finally, the Nons-Transformer model is improved by adding dilated causal convolution to its encoder, and the new set of IMF components, as well as the unreconstructed mid-frequency band IMF components and the reconstructed low-frequency IMF, component are used as inputs to the model to obtain the prediction results and perform error analysis. The experimental results show that our proposed model outperforms other single and combined models.

A comprehensive evaluation of machine learning and deep learning algorithms for wind speed and power prediction

Accurate wind speed and power predictions are crucial for renewable wind energy applications. This study compares and evaluates twelve machine learning (ML) and deep learning (DL) algorithms, including single and ensemble models across various time scales from 10 min to a day and a half ahead with a particular focus on ensemble prediction algorithms. Moreover, the study proposes a wind speed and power prediction system where the outcome of the wind speed prediction (WSP) model serves as input for the wind power prediction (WPP) model. Several evaluation metrics, such as mean absolute percentage error (MAPE) and mean square error (MSE) were calculated to benchmark different model accuracies. For WSP, the extremely randomized trees, decision tree, and bagging ensemble algorithms demonstrated high accuracy across different time scales where the MAPE ranged from 3.4% to 9.2%, the MSE ranged from 0.17 to 1.15, and the adjusted coefficient of determination ranged from 94% to 99%. For WPP, bagging ensemble algorithms and extremely randomized trees were also effective for predicting different time scales where the MAPE ranged from 4.12% to 11.7% and the MSE ranged from 10945 to 2.4. Ensemble ML algorithms provide better and more accurate results than single ML algorithms. The extreme gradient boosting model showed relatively small computational time and memory according to computational cost. Moreover, this study conducted a sensitivity analysis where air pressure, wind vane, and humidity were the key predictors for WSP and WPP.

A Transferable Federated Learning Approach for Wind Power Prediction Based on Active Privacy Clustering and Knowledge Merge

As wind power continues to develop, advancing wind power prediction becomes more and more crucial. This study focuses on advancing wind power prediction by addressing data privacy and enhancing model applicability at multi-spatial scales, from individual turbines to entire farms. Traditional methods are typically confined to a single scale, lacking flexibility in application, requiring extensive data from farms, which potentially compromises energy data privacy. To tackle these challenges, we introduce an innovative Divide-Merge Federated Learning with Active Private Clustering (D-M APCFed) approach. This approach strategically employs federated learning to train models within privacy-preserving boundary, overcoming the adverse effects of wind power data heterogeneity through a novel APC method and knowledge merge technique. The primary innovation of this study is a scalable and accurate wind power prediction model that operates effectively at multi-spatial scales while safeguarding energy data privacy. In case study of two spatial scales, the D-M APCFed approach achieves an average prediction accuracy of 87.11% in the twelve federated farms and 81.69% in the twenty federated turbines. This approach enables a more generalized model through the secure use of data from diverse sources at multi-spatial scales, enhancing prediction accuracy and ensuring the confidentiality of sensitive information.

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system. The data-driven forecasting methods are regarded as an effective solution. However, the inherent randomness and nonlinearity of wind power systems, along with the abundance of redundant information in measurement data, present challenges to forecasting methods. The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced data-driven forecasting models. Focus on the seasonal variation characteristics of wind energy, a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed. The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

Wind power forecasting using optimized LSTM by attraction–repulsion optimization algorithm

Wind power forecasting is crucial for energy conversion and management. This study employs the long short-term memory (LSTM) network, a specialized form of recurrent neural networks (RNNs) noted for its effectiveness in time-series prediction, to predict wind power from various turbines. Furthermore, we incorporate a cutting-edge metaheuristic optimization technique to optimize the training process of the LSTM, enhancing its parameter optimization. Specifically, we utilize the attraction–repulsion optimization algorithm (AROA), an innovative optimization algorithm inspired by the natural phenomena of attraction and repulsion for addressing complex optimization and engineering problems. This research applies the AROA to optimize the LSTM training process, which markedly improves the model's forecasting accuracy. Our analysis is conducted using four datasets from La Haute Borne wind turbines in France. The proposed AROA-LSTM model achieved R2 testing results of 0.9416, 0.9663, 0.9613, and 0.9622 for Turbines 1, 2, 3, and 4, respectively.

Enhancing Wind Power Forecasting Accuracy with Hybrid Deep Learning and Teaching-Learning-Based Optimization

Forecasting wind power generation is crucial for ensuring grid security and the competitiveness of the power market. This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. Using a real dataset spanning diverse weather conditions and turbine specifications collected between January 2018 and March 2020, the study employs 18 features as inputs, including Ambient Temperature, Wind Direction, and Wind Speed, with real power output in kW as the target variable. Metaheuristic algorithms including Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Biogeography-Based Optimization (BBO), and Firefly Algorithm (FA) are comprehensively compared for model optimization. TLBO-DL consistently provides forecasts that closely align with actual wind power values across instances, substantiated by its low RMSE of 98.7601, indicating effective minimization of errors in wind power forecasting. Comparative analysis with other algorithms reveals that TLBO-DL outperforms PSO-DL (RMSE: 102.6627), BMO-DL (RMSE: 132.4839), BBO-DL (RMSE: 103.8517), and FA-DL (RMSE: 104.7282) in terms of overall forecasting accuracy. The variations in the performance of other algorithms across instances highlight the robustness and effectiveness of TLBO-DL in achieving accurate wind power forecasts. Overall, TLBO-DL emerges as a reliable and superior algorithm for wind power forecasting, consistently providing accurate forecasts across a range of instances.

CrossGFA: wind power prediction with a multi-scale cross-graph network via a Frequency-Enhanced Channel attention mechanism

CrossGFA: wind power prediction with a multi-scale cross-graph network via a Frequency-Enhanced Channel attention mechanism

Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction

Accurate wind power forecasting is essential for efficient energy management and grid stability, enabling energy providers to balance supply and demand, optimize renewable energy integration, reduce operational costs, and enhance power grid reliability. The Echo State Network (ESN) is widely used for modeling nonlinear dynamic systems due to its simple and rapid training process. However, ESNs can be prone to system errors, leading to inaccurate models when handling high-order nonlinear complexities. To overcome this, we developed the Error Compensation Transfer Learning Echo State Network (ETL-ESN), which combines a computing layer based on ESN and a compensation layer using transfer learning. Our model identifies error auto-correlation as a key factor that increases variance in ESN predictions, and addresses this with an error compensation layer to reduce system errors. We further leverage transfer learning to prevent overfitting within the error domain. Extensive experiments using real-world wind power data demonstrate that the ETL-ESN model reduces training time from 65 s to 2 s compared to LSTM, while lowering MAE by up to 95%. The ETL-ESN achieves a 95% to 98% improvement in prediction accuracy across different turbines compared to traditional models. The code and datasets used in this study are available at GitHub repository https://github.com/zhuyingqin/Error-Transfer-ESN for further research and replication.

Short-term wind power prediction based on IBOA-AdaBoost-RVM

This study introduces an innovative model, namely IBOA-AdaBoost-RVM, which leverages the Improved Butterfly Optimization Algorithm (IBOA), Adaptive Boosting (AdaBoost), and Relevance Vector Machine (RVM). This model is used to solve the problem of low precision of wind power prediction. Initially, normalization is applied to reduce the influence of varying data dimensions. Subsequently, input variables are determined through the Pearson correlation method. Lastly, the efficacy of the introduced model is assessed across four distinct seasonal monthly data sets. The observed outcomes indicate that the proposed model outperforms other models in terms of evaluation metrics, with the average R2, RMSE, MAE, and MAPE values across the four datasets being 0.954, 10.403, 7.032, and 0.645, respectively, show that the proposed method has potential in the field of wind power prediction.

IDHNet: Ultra‐Short‐Term Wind Power Forecasting With IVMD–DCInformer–HSSA Network

ABSTRACTThe variability and unpredictability of wind power generation present significant challenges for grid management and planning. Enhancing the accuracy of wind power forecasting is crucial for improving the reliability of renewable energy systems. To enhance the accuracy of temporal wind power predictions, the IVMD–DCInformer–HSSA framework has been introduced. Initially, the original wind power data is decomposed into multiple intrinsic mode function (IMF) components using the improved variational mode decomposition (IVMD) technique. Subsequently, the Sparrow search algorithm (HSSA) is employed to optimize the parameters of the enhanced Informer deep neural network, which are then integrated into the improved Informer model. The predictions of each IMF component resulting from the IVMD decomposition are then combined to generate the final prediction outcome. The experimental results show that the R‐squared value of the proposed combined model is increased to 0.9903, and the accuracy is increased by 1%–3% compared with other models, which has a good prediction effect.