scholarly journals Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 68874-68882 ◽  
Author(s):  
Ning Li ◽  
Fuxing He ◽  
Wentao Ma ◽  
Ruotong Wang ◽  
Xiaoping Zhang
Keyword(s):  
Differential Evolution ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Cross Validation ◽  
Differential Evolution Algorithm ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Kernel Extreme Learning Machine ◽  
Evolution Algorithm ◽  
Learning Machine

Comparison of the Extreme Learning Machine with the BP Neural Network for Short-Term Prediction of Wind Power

2012 ◽  
Vol 608-609 ◽  
pp. 564-568 ◽  
Author(s):  
Yi Hui Zhang ◽  
He Wang ◽  
Zhi Jian Hu ◽  
Meng Lin Zhang ◽  
Xiao Lu Gong ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Power Prediction ◽  
Feed Forward Neural Network ◽  
Wind Power Prediction ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a new and effective single-hidden layer feed forward neural network learning algorithm. Extreme learning machine only needs to set the number of hidden layer nodes of the network, and there is no need to adjust the neural network input weights and the hidden units bias, and it generates the only optimum solution, so it has the advantage of fast learning and good generalization ability. And the back propagation (BP) neural network is the most maturely applied. This paper has introduced the extreme learning machine into the wind power prediction. By comparing the wind power prediction method using the BP neural network. Study shows that the extreme learning machine has better prediction accuracy and shorter model training time.

scholarly journals Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 109 ◽  
Author(s):  
Jiale Ding ◽  
Guochu Chen ◽  
Kuo Yuan
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Percentage Error ◽  
Grey Wolf ◽  
Power Prediction ◽  
Grey Wolf Optimization ◽  
Short Term ◽  
Wind Power Prediction ◽  
Learning Machine

In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.

scholarly journals Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p-Power Error Loss

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 673 ◽  
Author(s):  
Ning Li ◽  
Fuxing He ◽  
Wentao Ma
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Power System ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Bp Neural Network ◽  
Power Prediction ◽  
Error Loss ◽  
Wind Power Prediction ◽  
Learning Machine

In recent years, more and more attention has been paid to wind energy throughout the world as a kind of clean and renewable energy. Due to doubts concerning wind power and the influence of natural factors such as weather, unpredictability, and the risk of system operation increase, wind power seems less reliable than traditional power generation. An accurate and reliable prediction of wind power would enable a power dispatching department to appropriately adjust the scheduling plan in advance according to the changes in wind power, ensure the power quality, reduce the standby capacity of the system, reduce the operation cost of the power system, reduce the adverse impact of wind power generation on the power grid, and improve the power system stability as well as generation adequacy. The traditional back propagation (BP) neural network requires a manual setting of a large number of parameters, and the extreme learning machine (ELM) algorithm simplifies the time complexity and does not need a manual setting of parameters, but the loss function in ELM based on second-order statistics is not the best solution when dealing with nonlinear and non-Gaussian data. For the above problems, this paper proposes a novel wind power prediction method based on ELM with kernel mean p-power error loss, which can achieve lower prediction error compared with the traditional BP neural network. In addition, to reduce the computational problems caused by the large amount of data, principal component analysis (PCA) was adopted to eliminate some redundant data components, and finally the efficiency was improved without any loss in accuracy. Experiments using the real data were performed to verify the performance of the proposed method.

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