Accuracy Of Wind Power Prediction Research Articles

A pyramidal residual attention model of short‐term wind power forecasting for wind farm safety

AbstractWind power fluctuation significantly impacts the safe and stable operation of the wind farm power grid. As the installed capacity of grid‐connected wind power expands to a certain threshold, these fluctuations can detrimentally affect the wind farm's operations. Consequently, wind power prediction emerges as a critical technology for ensuring safe, stable and efficient wind power generation. To optimize power grid dispatching and enhance wind farm operation and maintenance, precise wind power prediction is essential. In this context, we introduce a joint deep learning model that integrates a compact pyramid structure with a residual attention encoder, aiming to bolster wind farm operational safety and reliability. The model employs a compact pyramid architecture to extract multi‐time scale features from the input sequence, facilitating effective information exchange across different scales and enhancing the capture of long‐term sequence dependencies. To mitigate vanishing gradients, the residual transformer encoder is applied, augmenting the original attention mechanism with a global dot product attention pathway. This approach improves the gradient descent process, making it more accessible without introducing additional hyperparameters. The model's efficacy is validated using a dataset from an actual wind farm in China. Experimental outcomes reveal a notable enhancement in wind power prediction accuracy, thereby contributing to the operational safety of wind farms.

A short-term wind power prediction approach based on an improved dung beetle optimizer algorithm, variational modal decomposition, and deep learning

Accurate short-term wind power prediction is crucial for the efficient and safe operation of wind power systems. To enhance the accuracy of short-term wind power prediction, this paper proposes a hybrid short-term wind power prediction model, IDBO-VMD-TCN-GRU-Attention, based on the Improved Dung Beetle Optimizer algorithm (IDBO), Variational Modal Decomposition (VMD), Temporal Convolutional Network (TCN), Gated Recurrent Unit (GRU), and Attention Mechanism. The IDBO is used to optimize the parameters of the VMD. Then the optimized IDBO-VMD is used to decompose the original data into modal components with lower volatility to fully extract the data features. These modal components are inputted into the TCN-GRU-Attention to predict the short-term wind power and obtain the final prediction value. The model is tested and validated using real data from four different months and compared against 11 other models. Results demonstrate that our proposed model significantly reduces the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by an average of at least 22.86 %, 18.82 %, and 19.99 %, respectively, across the four different months. This indicates that the proposed model provides a superior fit to the data, offers higher prediction accuracy, and holds significant practical value.

Prediction of wind and PV power by fusing the multi-stage feature extraction and a PSO-BiLSTM model

Accurate prediction of wind and photovoltaic (PV) power is essential to ensure the stable operation of power systems. However, the conventional power prediction methods face the challenges in capturing the stochastic fluctuations of wind and PV power. A wind and PV power prediction method fusing the multi-stage feature extraction and a particle swarm optimization (PSO)-bidirectional long and short-term memory (BiLSTM) model is developed. To illustrate the oscillation and instability of wind and PV power, the symplectic geometry model decomposition (SGMD) is presented to decompose the feature data and obtain multiple feature sub-sequences. Due to the excessive feature decomposition using the conventional methods, such as empirical modal decomposition, the kernel principal component analysis is utilized to reduce the dimensionality of the nonlinear sub-sequences of the wind and PV power. Compared to the traditional LSTM that ignores the inverse-time correlation between power and meteorological features and is sensitive to the model hyper-parameters, a power prediction model based on PSO-BiLSTM is developed. Using the power data from a power station in Xinjiang Province, China, as an example, the experimental results show that wind and PV power can be accurately forecasted using the developed method with high prediction accuracies of 97.9 % and 98.5 %, respectively.

Multi-temporal Scale Wind Power Forecasting Based on Lasso-CNN-LSTM-LightGBM

Due to the increasingly severe climate problems, wind energy has received widespread attention as the most abundant energy on Earth. However, due to the uncertainty of wind energy, a large amount of wind energy is wasted, so accurate wind power prediction can greatly improve the utilization of wind energy. To increase the forecast for wind energy accuracy across a range of time scales, this paper presents a multi-time scale wind power prediction by constructing an ICEEMDAN-CNN-LSTM-LightGBM model. Initially, feature selection is performed using Lasso regression to identify the most significant variables affecting the forecast for wind energy across distinct time intervals. Subsequently, the ICEEMDAN is utilized to break down the wind power data into various scales to capture its nonlinear and non-stationary characteristics. Following this, a deep learning model based on CNN and LSTM networks is developed, with the CNN responsible for extracting spatial features from the time series data, and the LSTM designed to capture the temporal relationships. Finally, the outputs of the deep learning model are fed into the LightGBM model to leverage its superior learning capabilities for the ultimate prediction of wind power. Simulation experiments demonstrate that the proposed ICEEMDAN-CNN-LSTM-LightGBM model achieves higher accuracy in multi-time scale wind power prediction, providing more reliable decision assistance with the management and operation of wind farms.

An Ultra-Short-Term Wind Power Prediction Method Based on Quadratic Decomposition and Multi-Objective Optimization

To augment the accuracy, stability, and qualification rate of wind power prediction, thereby fostering the secure and economical operation of wind farms, a method predicated on quadratic decomposition and multi-objective optimization for ultra-short-term wind power prediction is proposed. Initially, the original wind power signal is decomposed using a quadratic decomposition method constituted by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Fuzzy Entropy (FE), and Symplectic Geometry Mode Decomposition (SGMD), thereby mitigating the randomness and volatility of the original signal. Subsequently, the decomposed signal components are introduced into the Deep Bidirectional Long Short-Term Memory (DBiLSTM) neural network for time series modeling, and the Sand Cat Swarm Optimization Algorithm (SCSO) is employed to optimize the network hyperparameters, thereby enhancing the network’s predictive performance. Ultimately, a multi-objective optimization loss that accommodates accuracy, stability, and grid compliance is proposed to guide network training. Experimental results reveal that the employed quadratic decomposition method and the proposed multi-objective optimization loss can effectively bolster the model’s predictive performance. Compared to other classical methods, the proposed method achieves optimal results across different seasons, thereby demonstrating robust practicality.

LSTM Short-Term Wind Power Prediction Method Based on Data Preprocessing and Variational Modal Decomposition for Soft Sensors.

Soft sensors have been extensively utilized to approximate real-time power prediction in wind power generation, which is challenging to measure instantaneously. The short-term forecast of wind power aims at providing a reference for the dispatch of the intraday power grid. This study proposes a soft sensor model based on the Long Short-Term Memory (LSTM) network by combining data preprocessing with Variational Modal Decomposition (VMD) to improve wind power prediction accuracy. It does so by adopting the isolation forest algorithm for anomaly detection of the original wind power series and processing the missing data by multiple imputation. Based on the process data samples, VMD technology is used to achieve power data decomposition and noise reduction. The LSTM network is introduced to predict each modal component separately, and further sum reconstructs the prediction results of each component to complete the wind power prediction. From the experimental results, it can be seen that the LSTM network which uses an Adam optimizing algorithm has better convergence accuracy. The VMD method exhibited superior decomposition outcomes due to its inherent Wiener filter capabilities, which effectively mitigate noise and forestall modal aliasing. The Mean Absolute Percentage Error (MAPE) was reduced by 9.3508%, which indicates that the LSTM network combined with the VMD method has better prediction accuracy.

Prediction of wind energy with the use of tensor‐train based higher order dynamic mode decomposition

AbstractAs the international energy market pays more and more attention to the development of clean energy, wind power is gradually attracting the attention of various countries. Wind power is a sustainable and environmentally friendly resource of energy. However, it is unstable. Therefore, it is important to develop algorithms for its prediction. In this paper, we apply a recently developed algorithm that effectively combines the tensor train decomposition with the higher order dynamic mode decomposition (TT‐HODMD). The dynamic mode decomposition (DMD) is a data‐driven technique that does not need a prior mathematical model. It is based on the measurement data or time slots. As demonstrated, for prediction it is important to use the higher order DMD (HODMD). In turn, HODMD might lead to very large scale arrays that are sparse. The tensor train decomposition provides a highly efficient way to work with such arrays. It is demonstrated that the combined TT‐HODMD algorithm is capable of providing quite accurate prediction of wind power for months ahead.

Short-term Wind Power Forecasts based on VMD-KPCA and CFSBOA-BiLSTM

Accurate prediction of wind power is of great significance to reduce the impact of large-scale wind power connection and improve the security and stability of power grid. A short-term wind power forecasting method based on VMD-KPCA and CFSBOA-BiLSTM is proposed. Firstly, the key environmental factors restricting the volatility of wind power are fully considered, and the variational mode decomposition method is used to decompose the wind power and meteorological factors. Secondly, the kernel principal component analysis method is used to reduce the dimension and construct the sub-sequence characteristic data set. Finally, the multi-strategy improved butterfly algorithm is used to optimize the BiLSTM network hyperparameters and establish the CFSBOA-BiLSTM prediction model. The simulation analysis uses the measured data of a wind farm in northwest China. The experimental results show that the combined forecasting model can fully mine the hidden information of the data and improve the accuracy of short-term wind power forecasting. Compared with the BiLSTM model, RMSE, MAE and MAPE decreased by 60.37%, 66.6% and 67.59%, respectively.

Short-term wind power output prediction using hybrid-enhanced seagull optimization algorithm and support vector machine: A high-precision method

ABSTRACT Affected by the uncertainty of external environmental factors, wind power generation has significant characteristics of randomness and non-stationarity. Accurately predicting wind power is a necessary technical means to improve the efficiency of wind energy utilization and reduce wind abandonment. Integrating large-scale wind power into the grid has put higher requirements for wind power prediction accuracy. Hence, this study proposes a combined prediction model based on wavelet denoising (WD), hybrid enhanced seagull optimization algorithm (HESOA) and support vector machine (SVM) to predict short-term wind power. The WD technology obtains effective signals from original wind power data, providing a reasonable basis for prediction research. The HESOA is proposed by introducing the Tent mapping, individual self-learning factor and Gaussian population strategy into the original seagull optimization algorithm to optimize SVM’s prediction ability. The case study results reflect that the prediction method is feasible and universal in suppressing wind power generation volatility in spring and winter. The RMSE of short-term wind power prediction is controlled below 65 kW, and the value of R2 is above 98%. This study improves the accuracy of wind power prediction and helps to strengthen the renewable energy consumption level and reliability of wind power grid-connected generation systems.

A novel Bagged-CNN architecture for short-term wind power forecasting

ABSTRACT Wind power forecasting plays a pivotal role in ensuring the dependable integration of wind energy into power grids. This research introduces a novel Bagged-CNN architecture tailored for short-term wind power forecasting. This architecture harnesses the combined strengths of the Bagging ensemble technique and Convolutional Neural Networks (CNN) to enhance prediction accuracy and robustness. Our approach demonstrates superior forecasting capabilities by exploiting the spatial and temporal features captured by CNNs. To validate our proposed architecture, we have conducted a comprehensive evaluation across three distinct wind farms, each characterized by unique geographical and climatic conditions. We systematically compared the performance of our Bagged-CNN model against several standalone machine-learning algorithms commonly employed in wind power forecasting. The hyperparameters of our Bagged-CNN model are meticulously optimized using the GRID search algorithm, which systematically explores a predefined parameter space. Notably, our predictions shed light on the substantial impact of training data period length on wind power prediction accuracy. Our research underscores the potential of coupling Bagging and CNN techniques for short-term wind power forecasting. The Bagged-CNN architecture, as proposed, serves as a robust framework for capturing intricate spatial and temporal patterns in wind power data, thereby facilitating more precise and dependable predictions.

A novel adaptively combined model based on induced ordered weighted averaging for wind power forecasting

Accurate wind power prediction is a vital factor in day-ahead dispatch and increasing the high penetration of renewable energy integration. Some combined prediction models based on weight are proposed to provide uncertain information about wind power. However, the optimal weight issues to be addressed are still a tough problem. This paper proposes an adaptively combined prediction model based on the induced ordered weighted averaging operator. First, four classical prediction models are used as a predictor to forecast wind power. Second, the optimal weight is expressed as an optimization problem to minimize the prediction errors with the nonnegative constraints and summation constraint of weights, and the optimal optimization problem is transformed into a quadratic programming model, which can be easily solved to determine the optimal weights for multiple prediction models. Third, an interval prediction mode based on errors distribution produced by the proposed combined model with different confidence intervals is constructed, which can perform deterministic and probabilistic prediction of wind power simultaneously. Furthermore, the model's effectiveness is comprehensively evaluated using both deterministic and probabilistic approaches. This analysis is further enriched with a comprehensive examination of a year-round dataset obtained from a wind farm cluster in Ningxia, China. The data, recorded at 15-min intervals, offer robust evidence of the combined model's superior performance. The results show that the proposed approach can achieve higher accuracy than other benchmark prediction models, further verifying its reliability for wind power dispatch.

A hybrid model with combined feature selection based on optimized VMD and improved multi-objective coati optimization algorithm for short-term wind power prediction

With the continuous global increase in installed wind power capacity and subsequent surge in power generation, the contradiction between the safe operation of the grid and the efficient consumption of new energy after large-scale grid connection has become increasingly prominent. This paper presents a hybrid model prediction method to further improve the stability and accuracy of wind power prediction. Firstly, variational modal decomposition (VMD) optimized by the coati optimization algorithm (COA) is employed to decompose original wind power, mitigating the non-stationary characteristics of the power sequence. Subsequently, permutation entropy (PE) is used to recombine the decomposed components, and the combined feature selection method is achieved by integrating the Spearman correlation coefficient (SCC) and the autocorrelation function (ACF). Then, the multivariate combined model is constructed, and the improved multi-objective coati optimization algorithm (IMOCOA) determines the weight coefficients of each model to enhance the performance of the hybrid model. Finally, research and analysis are conducted from multiple scenarios and time scales using historical operating data from a wind farm in Xinjiang. The experimental results show that the proposed prediction model effectively improves the accuracy and stability of the wind power prediction compared with other popular prediction models.

Ultra-short term prediction of wind power based on multi-resolution closed-loop method

In order to improve the accuracy and stability of wind power prediction, a multi-resolution closed-loop wind power ultra-short term prediction method is proposed. Using historical data with different resolutions, the differential data is generated by the pre-predictor with low, medium and high resolutions, and then the differential data is combined with the post-prediction data into the post-predictor with three resolutions for training. Since the differential data is obtained from the prediction data with different resolutions, the post-prediction data containing the differential data contains inconsistent information between different resolutions, so that the post-predictor receives the inconsistency between different resolutions and minimizes the inconsistency after several closed-loop iterations, and finally outputs wind power prediction with different resolutions. At the end of the paper, simulation experiments are carried out using the data of the 2022 KDD Cup competition, and comparison is made with some mainstream wind power prediction models. It is found that the proposed model has high accuracy and stability.

Adaptive temporal transformer method for short-term wind power forecasting considering shift in time series distribution

In wind power prediction, the input probability distributions in the different sub-periods are shifted owing to the strong randomness of the input features, such as wind speed and direction. This may violate the assumption for machine learning that the training and test data meet the condition of being independent and identically distributed, resulting in an insufficient generalization ability of the prediction model that is trained with the training data and applied to unknown test data. To address this problem, this study proposes an adaptive temporal transformer method for short-term wind power forecasting. First, a temporal transformer model with a gate recurrent unit and multi-head attention layers was used to extract the short- and long-term temporal information of the multiple input variables. Then, an adaptive learning strategy consisting of two stages—temporal distribution characterization and temporal distribution matching—was developed to explore the common knowledge hidden in each sub-period. The case results for an actual wind farm in northwest China showed that the proposed method could effectively weaken the adverse effects of the shifts in time series distribution on forecasting and improve the accuracy and generalization of short-term wind power prediction.

A wind power forecasting model based on data decomposition and cross-attention mechanism with cosine similarity

In the context of societal advancement, escalating energy demands underscore the critical significance of exploring renewable resources. The accurate prediction of wind power is crucial for maximizing the utilization of wind energy and ensuring the smooth operation of wind farms. Currently, the majority of models are built upon architectures such as Transformer and Long-Short Term Memory (LSTM). However, Transformer-type models are not well-suited for time series forecasting, and LSTM often focuses on historical sequence information, exhibiting limited capability in capturing the relevant information between weather factors and power. Recognizing this inadequacy, this study proposes a novel model—a linear network architecture featuring a cosine-related cross-attention mechanism. The signal decomposition is carried out utilizing variational mode decomposition, with the determination of the optimal decomposition count guided by sample entropy analysis, named Improved Variational Mode Decomposition (IVMD). Subsequently, the conventional self-attention mechanism is replaced with a cross-attention mechanism. Contemporary mainstream models are primarily based on improved versions of core algorithms such as LSTM, Transformer, and Extreme Learning Machines. Experimental results show significant performance enhancements compared to these models across different timescales. In short-term, medium-term, and long-term forecasts, accuracy improves by more than 40 %, 20 %, and 5 %, respectively.

A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data

Supervisory Control and Data Acquisition (SCADA) system collects massive operation and environment information which directly or indirectly affects the output power in wind farms. Therefore, it becomes an imperious demand to analyze the underlying information from SCADA data for improving the performance of short-term wind power prediction. In this paper, an effective deep learning framework for short-term wind power forecasting based on SCADA data analysis is proposed. A data denoising scheme is designed based on wavelet decomposition. In this method, all SCADA signals (except the wind power signal itself) are decomposed into low-frequency component A and high-frequency component D respectively by the wavelet transform. Then, the maximum information coefficient (MIC) method is applied to choose features that have strong correlation with wind power. Finally, all the selected features and wind power are defined as input vector that are used to train long short-term memory networks. The simulation results based on real data extracted from a SCADA system installed in wind farm indicate that the designed deep learning framework can significantly improve the accuracy of short-term wind power prediction.

Short-term wind power prediction method based on CEEMDAN-GWO-Bi-LSTM

In order to improve the short-term prediction accuracy of wind power and provide the basis for power grid dispatching, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) -grey wolf optimization (GWO) -bidirectional long short-term memory network (Bi-LSTM) prediction model is proposed to predict the short-term output power of wind farms. Firstly, the original wind power data is preprocessed, and then the original wind power data is decomposed into components that are easy to extract features by using CEEMDAN. The Bi-LSTM prediction model is established for each component, and then the grey wolf optimization algorithm is used to optimize the parameters of the Bi-LSTM model. The optimized hyperparameters are brought into the Bi-LSTM model to output the prediction results of each component. Finally, the prediction results of each component are superimposed and reconstructed to obtain the final prediction results of wind power. The simulation analysis of the power data of a wind farm in Gansu Province shows that the CEEMDAN-GWO-Bi-LSTM model has better accuracy in short-term wind power prediction.

Research on the short-term wind power prediction with dual branch multi-source fusion strategy

The smooth operation of the power system imposes high requirements on wind power forecasting. Given the forecasting challenges brought by the coexistence of strong and weak disturbances in wind power data, this paper proposes a hierarchical anti-disturbance mechanism to raise the accuracy of single-point wind power prediction. In the first-level disturbance processing, we prioritize the treatment of strong disturbances in wind power data through a dual-branch multi-source fusion prediction structure. The wind power subsequences with distinct time–frequency characteristics are divided into two sets, processed by the stationary set prediction branch and the drastic set prediction branch, respectively. For the drastic set, we incorporate microenvironmental multi-source data surrounding the wind turbine as input and propose a parallel-structured attention fusion module CA (Convolution attention fusion block) to provide effective data support for the prediction module, then extract rich fusion features through deep temporal LSTM algorithm and ultimately achieve fine-grained forecasting. For weak disturbances that have not been adequately addressed in the previous step, we conduct a secondary treatment through error correction strategy. By employing the hierarchical processing approach, this paper accomplishes a comprehensive handling of disturbances with different intensities. Experimental results on four data sets show that the proposed model DEWFM (Dual-branch and error correction wind power forecast) demonstrates remarkable performance advantages in comparing with the baseline model and other advanced models, the average values of MAE reaching 1.679, revealing the effectiveness of the proposed method in enhancing the precision of short-term wind power prediction.

A GMEE-WFED System: Optimizing Wind Turbine Distribution for Enhanced Renewable Energy Generation in the Future

This paper presents the Generation Max Electrical Energy from Wind Friendly Environment Database (GMEE-WFED) system, a groundbreaking innovation aimed at enhancement the performance and energy output of wind power generation stations. The GMEE-WFED system has been meticulously designed to provide precise wind power forecasting within distributed turbine systems, facilitating the seamless integration of renewable energy into the grid. This forecasting is enabled by the utilization of the Spatial Dynamic Wind Power Forecasting (SDWPF) dataset, which takes into account the spatial distribution and dynamic characteristics of wind turbines. The GMEE-WFED system comprises five layers, each offering unique advantages. The first layer, referred to as the "Best Distribution of Turbines Based on DOA (BDT-DOA)," is designed to achieve the following objectives: (a) increase power generation, (b) determine the optimal coordinates (x, y) for each turbine, and (c) distribute turbines based on the best locations. The second layer, named the "Effect Features Layer (EF)," focuses on: (a) identifying the impact of features on wind power generation, (b) streamlining implementation time, and (c) reducing computational demands. The third layer, denoted as the "Average and Shifting up Target Layer (AEH-SUV)," serves the purposes of: (a) enhancing accuracy by calculating feature averages, and (b) predicting future active power through target shifting at different intervals (ranging from 1 to 6 h). Meanwhile, the fourth layer is associated with the development of a prediction model based on a deep learning technique known as "Deep Learning-Long Short-Term Memory (DL-LSTM) Layer," which is utilized for: (a) forecasting future energy production, (b) evaluating model accuracy at varying intervals, and (c) assessing overall model effectiveness. The final layer is also dedicated to constructing a prediction model, but it leverages a different deep learning technique called "Deep Learning Gate Recurrent Unit (DL-GRU)." These models contribute to accurate wind power predictions at various intervals and ensure the overall effectiveness of the system. Experimental results have shown that DL-GRU outperforms DL-LSTM in all shifting cases, underscoring the system's effectiveness in predicting future wind power generation and forecasting accuracy. As a result, the GMEE-WFED system is a pioneering approach that enhances wind DC-power generation forecasting. The GMEE-WFED system, with its intricate layers and advanced modeling techniques, represents a significant leap forward in harnessing the potential of wind energy for a more sustainable future.

Uncertainty Prediction of Offshore Wind Power Based on Outlier Processing, Data Noise Reduction and the Deep Learning Method

Background: Accurate prediction of offshore wind power is the basis for promoting the safe and economic operation of offshore wind farms. Objective: This paper proposes an uncertainty prediction learning model based on outlier processing, synchronous wavelet denoising and attention mechanism optimization to achieve accurate prediction of offshore wind power. Method: Firstly, the isolated forest is adopted to filter the outliers of offshore wind power data and delete the error data caused by equipment or humans. Secondly, a syn-chrosqueezing wavelet neural network (SWT) is applied to denoise historical wind power data, improve data quality, and lay a foundation for accurate prediction. Next, the offshore wind power prediction method based on IP-SO-LSTM-Attention is constructed to realize offshore wind power prediction, in which the attention mechanism is applied to focus on the influence of important features on the output of offshore wind power, and the improved particle swarm optimization algorithm is adopted to find the best network structure of LSTM-Attention to optimize the prediction effect. After predicting the point prediction results based on the SWT-IPSO-LSTM-Attention model, this paper sets MAPE, RMSE, MAE and other indicators to evaluate the prediction effect. Results: The prediction error MAPE of the proposed model is 4.12%. It is 63.21% higher than the benchmark model (SWT-BP) and 56.35% higher than the benchmark model (SWT-LSTM). Conclusion: Based on the point prediction results, this paper uses KDE (Gaussian) prediction results to calculate the out-put curve under different confidence levels and provides decisionmaking reference information for accurate decision-making.