Short-term wind power prediction using an improved grey wolf optimization algorithm with back-propagation neural network

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration

Accurate short-term forecasting of wind farm power generation is of great significance to economic development and stable operation of power system. In order to improve the accuracy of wind power prediction, this paper presents a short-term wind power prediction method based on empirical mode decomposition and deep learning. We firstly decompose historical data into two-dimensional tensors by using empirical mode decomposition, and then extract the local features of data by using convolutional neural network. The features are transmitted to the bidirectional long short-term memory for prediction. The measured wind power data form wind farm in Xinjiang are used for verification. We use root mean square error and mean absolute error as evaluation indexes to test the algorithm. Results of the experiment show that the reconstructed two-dimensional tensor is beneficial to the ability of convolutional neural network to extract local features, and the prediction accuracy of our method is higher than that of other traditional prediction algorithms.

To enhance the accuracy of short-term wind power prediction, this paper proposes a novel two-stage forecasting framework that integrates Sequential Variational Mode Decomposition (SVMD), Bayesian Optimization (BO), and a CNN-BiLSTM-Attention model. In the first stage, the preprocessed wind power historical data is decomposed into several modal components via SVMD. These components serve as inputs to the CNN-BiLSTM-Attention model, whose hyperparameters—including the learning rate, number of hidden units, and regularization coefficient—are automatically tuned using the BO algorithm. The output of this stage is the initial power prediction. In the second stage, the prediction error sequence from the first stage is analyzed and similarly processed (decomposed and modeled) to generate an error compensation term. The final prediction is obtained by summing the initial power prediction and the predicted error compensation. Results show that compared with the CNN-BiLSTM-Attention model, the MAE, MAPE and RMSE values of the improved CNN-BiLSTM-Attention two-stage prediction model decreased by 85.7%, 75.2% and 77.3%, respectively, demonstrating the effectiveness of the two-stage short-term wind power prediction method of the improved CNN-BiLSTM-Attention model studied in this paper. 为了提升短期风电功率预测的准确性，本文研究了一种基于逐次变分模态分解（SVMD）方法、贝叶斯优化（BO）算法和CNN-BiLSTM-Attention模型结合的两阶段短期风电功率预测方法。该方法具体原理为：在第一阶段，利用SVMD方法对经过数据预处理后的风电功率数据进行分解，将得到的模态分量作为CNN-BiLSTM-Attention短期功率预测模型的输入，然后引入BO算法对预测模型的学习率、隐含层节点和正则化参数进行调优，利用训练好的模型进行预测，得到初始风电功率预测值；在第二阶段，采用预测模型对误差序列进行误差补偿预测，得到初始误差功率预测数据，将初始预测功率和误差预测功率求和得到最终的风电功率预测结果。结果表明：相比于CNN-BiLSTM-Attention模型，改进CNN-BiLSTM-Attention模型两阶段预测模型的MAE值、MAPE值和RMSE值分别下降了85.7%、75.2%和77.3%，表明了本文研究的改进CNN-BiLSTM-Attention模型的两阶段短期风电功率预测方法的有效性。

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.

Considering that more than 90% of water vapor is concentrated in the troposphere, and many weather phenomena such as cloud, fog and rain occur in the middle and lower troposphere, a short-term wind power prediction method based on meteorological data of multiple isobaric surfaces in the middle and lower troposphere is proposed. In view of the fact that the power accuracy depends greatly on the accuracy of wind speed prediction, and the current wind speed prediction in wind farms has some problems such as phase lag and system deviation, the decoupling method of wind power conversion model is considered, which firstly corrects the wind speed prediction error and then establishes the wind power conversion model. In detail, based on the multi-layer meteorological forecast data, the optimal feature combination optimization model is established with the maximum average accuracy as the objective function. On this basis, a wind speed correction model based on Conv1D was constructed. Finally, the wind speed - power conversion model is constructed by five kinds of fluctuation process classification, and the corrected wind speed is converted into power. Experimental results show that the proposed method can obtain better accuracy than using correlation coefficients to select feature combinations or only using surface data for modeling.

This paper presents a method to improve the short-term wind power prediction at a given turbine using information from numerical weather prediction and from multiple observation points, which correspond to the locations of nearby turbines at a particular wind farm site. The prediction of wind power is achieved in two stages; in the first stage wind speed is predicted using our proposed method. In the second stage, the wind speed to output power conversion is accomplished using power curve model. The proposed wind power prediction method is tested using real measurements and NWP data from one of the wind farm sites in Australia. The performance is compared with the persistence and Grey predictor model in terms of Mean Absolute Error and Root Mean Square Error.

Large-scale grid connection of green and clean energy has become a development trend in the energy field. Accurate prediction of wind power output power can reduce the negative impact of wind power on the power system. In this paper, we use extreme learning machine method to achieve short-term prediction of wind power, and determine the optimal number of hidden layer neurons by lattice method. In order to verify the effectiveness of extreme learning machine neural network, we compare the simulation results of extreme learning neural network method and BP neural network method. Simulation results show that the prediction accuracy of extreme learning machine method is similar to that of BP neural network. At the same, considering that extreme learning machine only need to calculate the weight matrix from the hidden layer to the output layer, so extreme learning machine has certain computing advantage compared with BP neural networks.

Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore, this paper proposes a combined short-term wind power prediction model based on T-LSTNet_markov to improve prediction accuracy. First, perform data cleaning and data preprocessing operations on the original data. Second, forecast using T-LSTNet model in original wind power data. Finally, calculate the error between the forecast value and the actual value. The k-means++ method and Weighted Markov process are used to correct errors and to get the result of the final prediction. The data that are collected from a wind farm in Inner Mongolia Autonomous Region, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The empirical results show that the prediction accuracy is further improved after correcting errors.

Wind power prediction is important to wind power system operation with a large amount of wind power integration. Effective prediction for wind power can reduce the difficulty of grid dispatching. In this paper an advanced neural network model was proposed to predict the short-term output power of a single wind turbine in a wind farm. According to the relevant wind speed, wind direction, temperature, output power and other data obtained from the wind farm, the model was established to predict the output wind power ahead of 10 min and 1 h. The simulation results showed that the proposed advanced BP neural network model had a higher prediction accuracy comparing to the existing BP neural network model.KeywordsWind powerWind farmBP neural networkShort-term prediction

Connecting wind power to the power grid has recently become more common. To better manage and use wind power, its strength must be predicted precisely, which is of great safety and economic significance. In this paper, the short-term power prediction of wind power is based on self-adaptive niche particle swarm optimization (NPSO) in a neural net. Improved PSO adopts the rules of classification and elimination of a niche using a self-adaptive nonlinear mutation operator. Compared with the traditional method of maximum gradient, NPSO can skip a local optimal solution and approach the global optimal solution more easily in practice. Compared with the basic PSO, the number of iterations is reduced when the global optimal solution is obtained. The method proposed in this paper is experimentally shown to be capable of efficient prediction and useful for short-term power prediction.

The wind power prediction (WPP) is challenging as a large amount of data with complex nonlinear relationship should be fitted by the prediction method. To improve the accuracy, WPP based on the Multilayer Restricted Boltzmann Machine (MRBM), which is a deep learning neural network with strong feature interpretation ability, is presented in the paper. To explore the influencing factors of prediction accuracy, the number of hidden layers and the number of nodes in each layer of MRBM are studied. Furthermore, the classic Back Propagation Neural Network (BPNN) based WPP, as a reference, is compared with the MRBM method. The results show that the accuracy of MRBM based WPP is higher than that of BPNN based WPP. The Root Mean Square Error (RMSE) of the MRBM based prediction is 4.5% lower than that of BPNN in some period, and the error distribution of MRBM based WPP is with better concentration ability than that of BPNN based WPP.

Wind power prediction of wind farm plays a decisive role in stable electric power system operation.The BP neural network’s basic principle was introduced, and the numerical weather prediction (NWP) data and power data of wind farm as the training data of BP neural network was selected and trained; a linear regression model about the sample prediction error was presented, which considers the coupling relationship between the individual sample prediction error, the individual sample prediction error of BP neural network was selected as the regression factor, the individual sample prediction result of BP neural network was modified. As the modified prediction results performing, the prediction algorithm of short-term wind power considering the sample prediction error correction, has good self-learning and adaptive ability of BP neural network. It has overcome the shortcoming that the BP neural network has only considered the overall the prediction error of training samples, but without considered the prediction error of individual samples. This has further improved the prediction accuracy of BP neural network.

This paper uses a back propagation (BP) neural network to predict short-term wind power. Since the initial weights and thresholds of BP neural networks significantly impact their performance, we use the optimized particle swarm optimization (PSO) to obtain the critical parameters of BP neural networks. Specifically, we optimize the PSO to make it easier to get better parameters. The experimental results show that the BP neural network's mean relative error (MRE) is 11.91%, 15.18%, and 8.56%, respectively. In comparison, the MRE of the optimized BP neural network is 5.09%, 7.21%, and 4.44%.

Wind energy is known for its uncertainty and volatility, necessitating accurate wind speed prediction for stable wind farm operations. To enhance wind speed prediction accuracy, this study proposes a BP neural network (BPNN) short-term wind speed prediction model based on the Improved Dung Beetle Optimization (IDBO) algorithm. Addressing the issue of local optimization and reduced accuracy in the BPNN optimized by the Dung Beetle Optimization (DBO) algorithm, the circle chaotic mapping is utilized for population initialization to achieve a more uniform initial distribution. The improved sine-cosine algorithm, triangle wandering strategy, and adaptive weight coefficient are then employed to optimize dung beetle positions, balancing global exploration and local development capabilities and improving the algorithm’s search performance. Finally, the improved DBO algorithm optimizes the weights and thresholds of the BPNN, and the IDBO-BPNN prediction model was constructed. Simulation experiments were conducted based on wind speed data from a wind farm in Ohio, USA. The IDBO-BPNN model was compared with other prediction models, and error evaluation indexes were introduced to evaluate the experimental results. The findings demonstrate that the suggested model yields the most accurate predictions and achieves the optimal error evaluation indexes. MAE, MSE, RMSE, NSE and R2 of dataset 1 are 0.42247, 0.28775, 0.53642, 88.8785%, 89.161%, those of dataset 2 are 0.28283, 0.14952, 0.38668, 85.7383%, 86.577%, and those of dataset 3 are 0.45406, 0.39268, 0.62664, 84.3859%, 84.931%. In particular, compared with BPNN model, the five evaluation indexes of the IDBO-BPNN model promoted by 41.53%, 57.38%, 34.71%, 24.91%, and 11.44%, respectively in dataset 3. Therefore, that the proposed IDBO-BPNN model exhibits higher accuracy in short-term wind speed prediction, indicating its feasibility and superiority in the realm of wind energy.