Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

2014 ◽  
Vol 986-987 ◽  
pp. 524-528 ◽  
Author(s):  
Ting Jing Ke ◽  
Min You Chen ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Model Parameters ◽  
Short Term ◽  
Dynamic Prediction ◽  
Power Dynamic ◽  
Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power forecasting.

Wind Power Forecasting Based on the BP Neural Network

2013 ◽  
Vol 341-342 ◽  
pp. 1303-1307 ◽  
Author(s):  
Jian Dong Mao ◽  
Xiao Jing Zhang ◽  
Juan Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Engineering Application ◽  
Weather Forecast ◽  
Difficult Problem ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Accurate short-term wind power forecasting has important significance to safety, stability and economy of power system dispatching and also it is a difficult problem in practical engineering application. In this paper, by use of the data of numerical weather forecast, such as wind speed, wind direction, temperature, relative humidity and pressure of atmosphere, a short-term wind power forecasting system based on BP neural network has been developed. For verifying the feasibility of the system, some experiments have been were carried out. The results show that the system is capable of predicting accurately the wind power of future 24 hours and the forecasting accuracy of 85.6% is obtained. The work of this paper has important engineering directive significance to the similar wind power forecasting system.

Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Direction ◽  
Wind Farm ◽  
Prediction Method ◽  
Small World ◽  
Power Method ◽  
Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

A Model Based on BP Neural Network for Audible Noise Prediction

2014 ◽  
Vol 986-987 ◽  
pp. 1356-1359
Author(s):  
You Xian Peng ◽  
Bo Tang ◽  
Hong Ying Cao ◽  
Bin Chen ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Transmission Lines ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Absolute Error ◽  
Research Area ◽  
Noise Prediction ◽  
Bp Network ◽  
Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power

2015 ◽  
Vol 733 ◽  
pp. 893-897
Author(s):  
Peng Yu Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Pso Algorithm ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Combination Forecasting ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

scholarly journals A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

2016 ◽  
Vol 70 ◽  
pp. 10002 ◽  
Author(s):  
Jie Zeng ◽  
Chi Zhang ◽  
Ning Xie ◽  
Ping Yang ◽  
Chenyu Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Hybrid Model ◽  
Bp Neural Network ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Short-Term Wind Power Forecasting Model based on ICA-BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
Author(s):  
Zi-Cheng Lan ◽  
Yuan-Biao Zhang ◽  
Jing Zhang ◽  
Xin-Guang Lv
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Bp Neural Network ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Model Based ◽  
Power Forecasting
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