Short-Term Wind Speed Forecast Using Measurements From Multiple Turbines in A Wind Farm

Technometrics ◽  
2016 ◽  
Vol 58 (1) ◽  
pp. 138-147 ◽  
Author(s):  
Arash Pourhabib ◽  
Jianhua Z. Huang ◽  
Yu Ding
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Short Term ◽  
Wind Speed Forecast

Short-Term Wind Speed Forecasting by Using Chaotic Theory and SVM

2013 ◽  
Vol 300-301 ◽  
pp. 842-847 ◽  
Author(s):  
Cai Hong Zhu ◽  
Ling Ling Li ◽  
Jun Hao Li ◽  
Jian Sen Gao
Keyword(s):  
Phase Space ◽  
Wind Speed ◽  
Wind Farm ◽  
Phase Space Reconstruction ◽  
Percentage Error ◽  
Support Vector ◽  
Forecasting Model ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Wind Speed Forecast

The wind speed forecast is the basis of the wind power forecast. The wind speed has the characteristics of random non-smooth so obviously that its precise forecast is extremely difficult. Therefore, a forecasting method based on the theory of chaotic phase-space reconstruction and SVM was put forward in this paper and a forecasting model of Chaotic Support Vector Machine was built. In order to improve the precision and generalization ability, the key parameters in the phase space reconstruction and the key parameters of SVM were carried out joint optimization by using particle swarm algorithm in the paper. Then the optimal parameters were brought into the forecasting model to forecast short-term wind speed. The above method was applied to wind speed forecast of a wind farm in Inner Mongolia, China. In the experiments of computer simulation, the absolute percentage error of forecasting results was only 12.51%, which showed this method was effective for short-term wind speed forecast.

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 Short-Term Wind Speed Forecast With Low Loss of Information Based on Feature Generation of OSVD

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 81027-81046 ◽  
Author(s):  
Nantian Huang ◽  
Yinyin Wu ◽  
Guowei Cai ◽  
Heyan Zhu ◽  
Changyong Yu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Feature Generation ◽  
Low Loss ◽  
Short Term ◽  
Loss Of Information ◽  
Wind Speed Forecast

Short-Term Wind Speed and Power Prediction Using Fuzzy Information Granulation-Support Vector Machine

2012 ◽  
Vol 608-609 ◽  
pp. 814-817
Author(s):  
Xiao Fu ◽  
Dong Xiang Jiang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Farm ◽  
Support Vector ◽  
Fuzzy Information ◽  
Short Term ◽  
Power Fluctuation ◽  
Data Set ◽  
Information Granulation ◽  
Fuzzy Information Granulation

The power fluctuation of wind turbine often causes serious problems in electricity grids. Therefore, short term prediction of wind speed and power as to eliminate the uncertainty determined crucially the development of wind energy. Compared with physical methods, support vector machine (SVM) as an intelligent artificial method is more general and shows better nonlinear modeling capacity. A model which combined fuzzy information granulation with SVM method was developed and implemented in short term future trend prediction of wind speed and power. The data, including the daily wind speed and power, from a wind farm in northern China were used to evaluate the proposed method. The prediction results show that the proposed model performs better and more stable than the standard SVM model when apply them into the same data set.

scholarly journals Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhongxian Men ◽  
Eugene Yee ◽  
Fue-Sang Lien ◽  
Zhiling Yang ◽  
Yongqian Liu
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Weather Prediction ◽  
Northern China ◽  
Short Term ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Power Forecasting

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

scholarly journals Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jianzhou Wang ◽  
Qingping Zhou ◽  
Haiyan Jiang ◽  
Ru Hou
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Wind Farm ◽  
Hypothesis Test ◽  
Recurrence Plot ◽  
Support Vector ◽  
Short Term ◽  
Cuckoo Optimization Algorithm ◽  
Input Variables

This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP) and optimized support vector regression (SVR). Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA), particle swarm optimization algorithm (PSO), and cuckoo optimization algorithm (COA). Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1) analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2) the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3) the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.

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