A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm

Peng, Xiaosheng; Liu, Cong; Jia, Shiyuan; Zhou, Liangsong; Wang, Bo; Che, Jianfeng

doi:10.1002/we.2761

Cited by 11 publications

(5 citation statements)

References 37 publications

(73 reference statements)

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“…The factor exposure value attribute in the data set has a very large scale difference; we use the max-min scaling to divide the original data and perform normalized scaling, re-scaling the value to make its final range between 0 and 1, and ultimately using the factor standard score to calculate the asset correlation, avoiding the transition fitting and thus improving the accuracy. The normalization process for the maximum and minimum values is carried out as follows: (12) where X ′ is the normalized data, X is the original data, and X max and X min are the maximum and minimum values.…”

Section: Feature Selectionmentioning

confidence: 99%

“…These correlation-dependent conditions have a significant impact on the overall accuracy of the forecast. In addition, traditional AI models have limitations in handling large-scale and long-term data, dealing with multidimensional spatio-temporal data, and mitigating problems such as gradient vanishing, gradient explosion, and overfitting [12]. This also affects the accuracy of stock correlation predictions.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

Luo,

Zhong,

Wang

et al. 2024

IEEE Access

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This study utilizes a new approach for short-term stock correlation forecasting using a combination of convolutional neural networks (CNN), bi-directional long and short-term memory (BiLSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series data. The objective is to improve the accuracy of short-term equity correlation forecasts, which is essential for the efficient management of financial asset portfolios. The existing time series models usually only take into account the effect of stock historical data characteristics on stock correlation but ignore the fact that stock correlation is affected by multiple factors and undergoes an unknown unstable trend. More efficient methods or algorithms need to be devised to deal with financial data. This paper explores stock metrics and introduces a multi-factor model to quantify the factors affecting stock returns in order to calculate the correlation between stocks. Meanwhile, to cope with the problem of incomplete input data, this paper simultaneously incorporates stock return data that is directly related to the correlation data. In the CNN-BiLSTM-Attention (CLATT) model, the role of the CNN module is to obtain high-dimensional features from the input time series data, and the role of the BiLSTM module is to process the time series information to capture the time-series features in the stock return data and correlation data in the sample. In order to acquire the inherent correlation between the input stock return data and correlation data and to enhance the prediction accuracy of the model, the attention mechanism is finally implemented and used to optimize the weights of the BiLSTM output. In this paper, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) metrics, mean absolute percentage of error (MAPE), and the coefficient of determination R 2 , as well as four benchmark models, are used to evaluate the performance of the proposed model. The result shows that the CLATT model has 57.32% and 33% improvement in the correlation prediction accuracy of different stock portfolios compared to the single LSTM model, and the model prediction accuracy has been improved. Compared with the benchmark CNN-BiLSTM, it also has 56.06% and 28.87% improvement.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

Luo,

Zhong,

Wang

et al. 2024

IEEE Access

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“…A convolutional neural network (CNN) then captures spatial features and generates final predictions. Peng et al [5] proposed a three-step multi-integration algorithm combining DL and ensemble learning. They use variational mode decomposition (VMD) and wavelet transform (WT) to decompose original data and construct sub-models with stacked denoising autoencoder (SDAE), LSTM, and bidirectional long short-term memory (BLSTM).…”

Section: Related Workmentioning

confidence: 99%

Wind Power Prediction with ETSformer

Kramer,

Baumann

2023

ESANN 2023 Proceesdings

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With growing environmental awareness, power generation from wind and other renewable sources is becoming increasingly important. Accurate short-term predictions of wind turbine power are needed to keep the grid stable and secure. This paper investigates the use of ETSformer, a time series approach based on the transformer architecture, for wind power prediction. ETSformer incorporates exponential smoothing principles and introduces mechanisms such as exponential smoothing attention and frequency attention to improve accuracy, efficiency and interpretability. This study compares ETSformer and LSTM on a sample dataset of a wind farm and its surrounding sites within a three kilometer radius from the Wind Integration National Dataset Toolkit with five minute interval measurements. The investigation shows promising results and improvements of ETSformer in ultra-short and short-term wind power prediction.

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“…Wind power prediction can provide data support for wind farm production and power dispatch by predicting future wind power output, while high precision and high‐efficiency wind power prediction can quickly provide accurate numerical predicts for relevant enterprises and departments, and adjust corresponding power generation strategies to reduce the abandoned wind rate through highly reliable predicts, 3 thereby reducing enterprise losses and ensuring grid security. However, wind power data are affected by the volatility and randomness of wind, and its data have serious nonstationary characteristics, which makes it impossible to achieve highly accurate predicts 4,5 . To address the nonstationary nature of wind power data, relevant studies have pointed out that signal decomposition can achieve the stabilization of nonstationary data 6–8 .…”

Section: Introductionmentioning

confidence: 99%

“…However, wind power data are affected by the volatility and randomness of wind, and its data have serious nonstationary characteristics, which makes it impossible to achieve highly accurate predicts. 4,5 To address the nonstationary nature of wind power data, relevant studies have pointed out that signal decomposition can achieve the stabilization of nonstationary data. [6][7][8] The empirical mode decomposition (EMD) algorithm is one of the most classical signal decomposition algorithms, however, it suffers from modal aliasing and endpoint effects.…”

Section: Introductionmentioning

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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A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm

Cited by 11 publications

References 37 publications

Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

Wind Power Prediction with ETSformer

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

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