scholarly journals Error Analysis of Ultra Short Term Wind Power Prediction Model

2017 ◽  
Author(s):  
Xiaofan Zhu ◽  
Xiaoming Zha ◽  
Liang Qin
Keyword(s):  
Error Analysis ◽  
Prediction Model ◽  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

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 Short-term Wind Power Prediction Model Based on Encoder-Decoder LSTM

2018 ◽  
Vol 186 ◽  
pp. 012020
Author(s):  
Kuan Lu ◽  
Wen Xue Sun ◽  
Xin Wang ◽  
Xiang Rong Meng ◽  
Yong Zhai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Wind Power Prediction

Short-Term Wind Power Prediction and Error Analysis

2013 ◽  
Vol 448-453 ◽  
pp. 1851-1857
Author(s):  
Rui Ma ◽  
Ling Ling Wang ◽  
Shu Ju Hu
Keyword(s):  
Error Analysis ◽  
Wind Power ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Power Prediction ◽  
System Operation ◽  
Short Term ◽  
Power System Operation ◽  
Wind Power Prediction ◽  
Time Series Method

The prediction accuracy of wind power is important to the power system operation. Based on BP neural network used to forecast directly and time-series method used to forecast indirectly, the output wind power prediction of 4 hours in advance was studied in this paper. Simulation results showed that the performance of direct prediction is better, and the reason for that was analyzed in the paper. Finally, error analysis of prediction was researched. Comprehensive evaluation of prediction error which contains horizontal and longitudinal error evaluation was proposed.

Error analysis of short term wind power prediction models

2011 ◽  
Vol 88 (4) ◽  
pp. 1298-1311 ◽  
Author(s):  
Maria Grazia De Giorgi ◽  
Antonio Ficarella ◽  
Marco Tarantino
Keyword(s):  
Error Analysis ◽  
Wind Power ◽  
Prediction Models ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

scholarly journals Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 109 ◽  
Author(s):  
Jiale Ding ◽  
Guochu Chen ◽  
Kuo Yuan
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Percentage Error ◽  
Grey Wolf ◽  
Power Prediction ◽  
Grey Wolf Optimization ◽  
Short Term ◽  
Wind Power Prediction ◽  
Learning Machine

In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.

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