scholarly journals Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting

2018 ◽  
Vol 10 (12) ◽  
pp. 4601 ◽  
Author(s):  
Yuewei Liu ◽  
Shenghui Zhang ◽  
Xuejun Chen ◽  
Jianzhou Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Wind Farm ◽  
Linear Models ◽  
Search Algorithm ◽  
Forecasting Model ◽  
Neural Network Models ◽  
Wind Speed Forecasting ◽  
Model Based

The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. Through research, it is found that the time series of wind speed demonstrate not only linear features but also nonlinear features. Hence, a combined forecasting model based on an improved cuckoo search algorithm optimizes weight, and several single models—linear model, hybrid nonlinear neural network, and fuzzy forecasting model—are developed in this paper to provide more trend change for time series of wind speed forecasting besides improving the forecasting accuracy. Furthermore, the effectiveness of the proposed model is proved by wind speed data from four wind farm sites and the results are more reliable and accurate than comparison models.

Wind Speed Forecasting in Wind Farm

2014 ◽  
Vol 672-674 ◽  
pp. 306-309
Author(s):  
Hong Peng Liu ◽  
Xiao Di Zhang ◽  
Hong Sheng Li ◽  
Qing Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Rbf Neural Network ◽  
Forecasting Model ◽  
Training Time ◽  
Wind Speed Forecasting ◽  
Forecasting Models ◽  
Artificial Neural Network Method

Artificial neural network method was used to forecast the wind speed, and two wind speed forecasting models were built based on BP and RBF neural network methods. 24 hours continuous wind speed forecast was conducted for a single wind turbine in wind farm. The results show that the models built are reasonable and have high prediction accuracy. By comparing the two kinds of wind speed forecasting models, BP neural network forecasting model has higher prediction accuracy than RBF neural network forecasting model in wind speed, but it demands much more training time.

Study on Wind Speed Forecasting Based on STC and BP Neural Network

2013 ◽  
Vol 724-725 ◽  
pp. 623-629
Author(s):  
Xing Jie Liu ◽  
Wen Shu Zheng ◽  
Tian Yun Cen
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Target Prediction ◽  
Temporal Correlation ◽  
Economic Security ◽  
Wind Speed Forecasting ◽  
Spatio Temporal

Accurate wind speed forecasting of wind farm is of great significance in economic security and stability of the grid. In order to improve the prediction accuracy, the paper first proposed a spatio-temporal correlation predictor method. Based on physical characteristics of wind speed evolution, the method looked for the wind speed and direction information at sites close to the target prediction site, and established STCP model to forecast. And then we established the BP neural network to finish multi-step forecast with wind speed time series of target forecast site .Last, two methods were combined to form STCP-BP method. Simulation tests are conducted with operation data from certain wind farm group in China and results show that STCP-BP method can effectively improve the prediction accuracy compared with BP model.

scholarly journals Research on Combined Model Based on Multi-Objective Optimization and Application in Wind Speed Forecast

2019 ◽  
Vol 9 (3) ◽  
pp. 423 ◽  
Author(s):  
Shenghui Zhang ◽  
Yuewei Liu ◽  
Jianzhou Wang ◽  
Chen Wang
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Linear Models ◽  
Time Series Data ◽  
Mean Absolute Percentage Error ◽  
Series Data ◽  
Percentage Error ◽  
Combined Model ◽  
Absolute Percentage Error ◽  
Model Based

Wind power is an important part of a power system, and its use has been rapidly increasing as compared with fossil energy. However, due to the intermittence and randomness of wind speed, system operators and researchers urgently need to find more reliable wind-speed prediction methods. It was found that the time series of wind speed not only has linear characteristics, but also nonlinear. In addition, most methods only consider one criterion or rule (stability or accuracy), or one objective function, which can lead to poor forecasting results. So, wind-speed forecasting is still a difficult and challenging problem. The existing forecasting models based on combination-model theory can adapt to some time-series data and overcome the shortcomings of the single model, which achieves poor accuracy and instability. In this paper, a combined forecasting model based on data preprocessing, a nondominated sorting genetic algorithm (NSGA-III) with three objective functions and four models (two hybrid nonlinear models and two linear models) is proposed and was successfully applied to forecasting wind speed, which not only overcomes the issue of forecasting accuracy, but also solves the difficulties of forecasting stability. The experimental results show that the stability and accuracy of the proposed combined model are better than the single models, improving the mean absolute percentage error (MAPE) range from 0.007% to 2.31%, and the standard deviation mean absolute percentage error (STDMAPE) range from 0.0044 to 0.3497.

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