A case study on a hybrid wind speed forecasting method using BP neural network

2011 ◽  
Vol 24 (7) ◽  
pp. 1048-1056 ◽  
Author(s):  
Zhen-hai Guo ◽  
Jie Wu ◽  
Hai-yan Lu ◽  
Jian-zhou Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Wind Speed Forecasting ◽  
Forecasting Method

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 Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

2019 ◽  
Vol 9 (1) ◽  
pp. 126 ◽  
Author(s):  
Qiong Qin ◽  
Xu Lai ◽  
Jin Zou
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Generation System ◽  
Forecasting Accuracy ◽  
Statistical Forecasting ◽  
Wind Speed Forecasting ◽  
Proposed Model ◽  
Forecasting Method ◽  
Svm Model ◽  
Power Generation System

Accurate wind speed forecasting is of great significance for a reliable and secure power generation system. In order to improve forecasting accuracy, this paper introduces the LSTM neural network and proposes a wind speed statistical forecasting method based on the EEMD-FuzzyEn-LSTMNN model. Moreover, the MIC is used to analyze the autocorrelation of wind speed series, and the predictable time of wind speed statistical forecasting method for direct multistep forecasting is taken as four hours. In the EEMD-FuzzyEn-LSTMNN model, the original wind speed series is firstly decomposed into a series of components by using EEMD. Then, the FuzzyEn is used to calculate the complexity of each component, and the components with similar FuzzyEn values are classified into one group. Finally, the LSTMNN model is used to forecast each subsequence after classification. The forecasting result of the original wind speed series is obtained by aggregating the forecasting result of each subsequence. Three forecasting cases under different terrain conditions were selected to validate the proposed model, and the BPNN model, the SVM model and the LSTMNN model were used for comparison. The experimental results show that the forecasting accuracy of the EEMD-FuzzyEn-LSTMNN model is much higher than that of the other three models.

Short-term wind speed forecasting based on long short-term memory and improved BP neural network

2022 ◽  
Vol 134 ◽  
pp. 107365
Author(s):  
Gonggui Chen ◽  
Bangrui Tang ◽  
Xianjun Zeng ◽  
Ping Zhou ◽  
Peng Kang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Speed Forecasting ◽  
Long Short Term Memory
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