Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network

Zheng, Shuang; Jia, Zhaoju; Zhang, Ziwei; Liu, Fugang; Long, Han

doi:10.1007/978-3-030-62483-5_26

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…BP neural network is the most classic neural network. In the current field of wind power prediction, many studies use BP neural networks as the prediction model for single‐feature wind power prediction 39,40 . The core of BP neural network is “forward prediction, reverse correction,” with a three‐layer network structure, including input layer, hidden layer, and output layer.…”

Section: Proposed Methodsmentioning

confidence: 99%

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Zhang,

Wang,

et al. 2023

Energy Science & Engineering

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Wind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments.

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Section: Proposed Methodsmentioning

confidence: 99%

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Zhang,

Wang,

et al. 2023

Energy Science & Engineering

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show abstract

“…The current wind power forecasting models are, on the one hand, constrained by the source data of wind farms, and tend to ignore the influence of various environmental factors on wind power, resulting in the multivariate series of environmental factors not being used effectively. On the other hand, due to the nonlinear variation of the wind power and multivariate environmental information series [1][2][3], the convergence of forecasting models gradually slows down, and problems with over-fitting problem can occur with increased in input variables.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

2022

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Improving the accuracy of wind power forecasting can guarantee the stable dispatch and safe operation of the grid system. Here, we propose an EMD-PCA-RF-LSTM wind power forecasting model to solve problems in traditional wind power forecasting such as incomplete consideration of influencing factors, inaccurate feature identification, and complex space–time relationships between variables. The proposed model incorporates Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), Random Forest (RF), and Long Short-Term Memory (LSTM) neural networks, And environmental factors are filtered by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm when pre-processing the data. First, the environmental factors are extended by the EMD algorithm to reduce the non-stationarity of the series. Second, the key influence series are extracted by the PCA algorithm in order to remove noisy information, which can seriously interfere with the data regression analysis. The data are then subjected to further feature extraction by calculating feature importance through the RF algorithm. Finally, the LSTM algorithm is used to perform dynamic time modeling of multivariate feature series for wind power forecasting. The above combined model is beneficial for analyzing the effects of different environmental factors on wind power and for obtaining more accurate prediction results. In a case study, the proposed combined forecasting model was verified using actual measured data from a power station. The results indicate that the proposed model provides the most accurate results when compared to benchmark models: MSE 7.26711 MW, RMSE 2.69576 MW, MAE 1.73981 MW, and adj-R2 0.9699203s.

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An electric power forecasting method based on dual time series attention mechanism neural network structure

Zhan

Lei

Kou

2021

2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C)

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Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network

Cited by 3 publications

References 11 publications

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

An electric power forecasting method based on dual time series attention mechanism neural network structure

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