A Chaotic BP Neural Network Used to Wind Power Prediction

2018 ◽  
Author(s):  
Lina Zhu ◽  
Hongtao Shi ◽  
Maosheng Ding
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Power Prediction ◽  
Wind Power Prediction

scholarly journals Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 157 ◽  
Author(s):  
Pei Zhang ◽  
Yanling Wang ◽  
Likai Liang ◽  
Xing Li ◽  
Qingtian Duan
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Linear Regression Method ◽  
Power Prediction ◽  
Short Term ◽  
Outlier Identification ◽  
Dbscan Algorithm ◽  
Wind Power Prediction

Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction.

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.

Application and Contrast Analysis of BP and RBF Neural Network in Short-Term Wind Power Prediction

2014 ◽  
Vol 492 ◽  
pp. 544-549 ◽  
Author(s):  
Wen Hua Li ◽  
Qian Xiao ◽  
Jin Long Liu ◽  
Hui Qiao Liu
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Farm ◽  
Rbf Neural Network ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Contrast Analysis ◽  
Bp Model ◽  
Better Than

Wind power prediction is very important to maintain the power balance and economic operation of power system. The BP and RBF neural network were respectively used to predict one wind turbines’ output power, in 4 hours, on a wind farm in Shandong Province. The results show that the BP model, with 6-13-1 net structure and considering the meteorological factors, exhibits the best prediction accuracy (MAPE is 3.59%, NRMSE is 1.58%). The most important factor in the meteorological information for power prediction is temperature, followed by air pressure, relative humidity finally. BP model is slightly better than RBF model, but the latter is much better in the learning speed and stability. Dynamic-BP neural network, combined with the dynamical weight adjustment method, is better than BP neural network in solving the weight problem. These methods are feasible to the 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.

Research on Wind Power Prediction by Combining Mesoscale Numerical Model with Neural Network Model

2012 ◽  
Vol 512-515 ◽  
pp. 771-777
Author(s):  
Feng Ming Yu ◽  
Xi Cang Li ◽  
Jin Hua Song ◽  
Chun Xiang Gao ◽  
Chun Long Jiang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Correlation Coefficient ◽  
Bp Neural Network ◽  
Wind Farm ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Relative Root

Effective wind power prediction on wind farm can not only guarantee safe operation of wind farm, but also increase wind power storage and utilization efficiency. This research combines mesoscale numerical weather prediction model with BP neural network model for the use of wind power prediction. WRF model is used to recalculate the meteorological elements of trial wind farm from Jun. 2008 to Jun. 2009, and the accuracy check result shows that the correlation coefficient between predicted value and corresponding measured value of wind speed reaches 0.72. Predictions accuracy of wind direction, air temperature, humidity and air pressure are also precise, which meets the requirement of building BP neural network prediction model. The BP neural network prediction models of output power of 40 wind turbines are established on trial wind farm one by one, to analyze the influence of data normalization method and neuron number at the hidden layer on prediction accuracy. The prediction test every 10 minutes, with the actual effect of 24 hours, is done for 26 days, and prediction accuracy test is conducted by using independent samples. The result shows that relative root mean square error of the output power of the single wind turbine from 24.8% to 32.6%, and the correlation coefficient between predicted value and measured value is from 0.45 to 0.68; relative root mean square error of the whole wind farm is 21.5%, and the correlation coefficient between predicted value and measured value is 0.74.

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