Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM

Wu, Qinghe; Guan, Fengying; Lv, Chen; Huang, Yuehui

doi:10.1049/rpg2.12085

Cited by 127 publications

(35 citation statements)

References 31 publications

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“…CNN is a feedforward neural network, which is composed of input layer, convolution layer, pooling layer, (Solas et al 2019). In order to ensure the prediction accuracy of LCWGAN-GP method, convolutional neural network (CNN) with good fitting performance to nonlinear function is selected as the discrimination model to form the nonlinear mathematical relationship function between historical time series data and future wind power prediction value (Wu et al 2021), as shown in Fig. 5.…”

Section: Construction Of Discriminant Modelmentioning

confidence: 99%

Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP

Huang

Wei

et al. 2022

Soft Comput

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A short-term wind power prediction model based on BiLSTM–CNN–WGAN-GP (LCWGAN-GP) is proposed in this paper, aiming at the problems of instability and low prediction accuracy of short-term wind power prediction. Firstly, the original wind energy data are decomposed into subsequences of natural mode functions with different frequencies by using the variational mode decomposition (VMD) algorithm. The VMD algorithm relies on a decision support system for the decomposition of the data into natural mode functions. Once the decomposition is performed, the nonlinear and dynamic behavior are extracted from each natural mode function. Next, the BiLSTM network is chosen as the generation model of the generative adversarial network (WGAN-GP) to obtain the data distribution characteristics of wind power’s output. The convolutional neural network (CNN) is chosen as the discrimination model, and the semi-supervised regression layer is utilized to design the discrimination model to predict wind power. The minimum–maximum game is formed by the BiLSTM and CNN network models to improve the quality of sample generation and further improve the prediction accuracy. Finally, the actual data of a wind farm in Jiuquan City, Gansu Province, China is taken as an example to prove that the proposed method has superior performance compared with other prediction algorithms.

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Section: Construction Of Discriminant Modelmentioning

confidence: 99%

Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP

Huang

Wei

et al. 2022

Soft Comput

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“…Noman et al [18] investigated a support vector machine (SVM)-based regression algorithm for predicting wind power in Estonia one day in advance. Wu et al [19] suggested a new spatiotemporal correlation model (STCM) for ultrashort-term wind power prediction based on convolutional neural networks and long short-term memory (CNN-LSTM). The STCM based on CNN-LSTM has been used for the collection of metrological factors at various places.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

et al. 2021

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In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.

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“…They compared the predicted power tables with the available data on the actual turbine power output measured in the wind farm. Wu et al [19] studied the effective parameters for optimizing the output power of wind turbines and compared the results with the experimental data available from a wind farm in China. They concluded that the LSTM method can accurately detect values, is very accurate and fast, and is more efficient at predicting values than other existing methods.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory

et al. 2021

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From conventional turbines to cutting-edge bladeless turbines, energy harvesting from wind has been well explored by researchers for more than a century. The vortex bladeless wind turbine (VBT) is considered an advanced design that alternatively harvests energy from oscillation. This research investigates enhancing the output electrical power of VBT through simulation of the fluid–solid interactions (FSI), leading to a comprehensive dataset for predicting procedure and optimal design. Hence, the long short-term memory (LSTM) method, due to its time-series prediction accuracy, is proposed to model the power of VBT from the collected data. To find the relationship between the parameters and the variables used in this research, a correlation matrix is further presented. According to the value of 0.3 for the root mean square error (RMSE), a comparative analysis between the simulation results and their predictions indicates that the LSTM method is suitable for modeling. Furthermore, the LSTM method has significantly reduced the computation time so that the prediction time of desired values has been reduced from an average of two and a half hours to two minutes. In addition, one of the most important achievements of this study is to suggest a mathematical relation of output power, which helps to extend it in different sizes of VBT with a high range of parameter variations.

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Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM

Cited by 127 publications

References 31 publications

Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP

Short-term prediction of wind power based on BiLSTM–CNN–WGAN-GP

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory

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