Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network

2020 ◽  
pp. 245-254
Author(s):  
Shuang Zheng ◽  
Zhaoju Jia ◽  
Ziwei Zhang ◽  
Fugang Liu ◽  
Long Han
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Wind Power ◽  
Bp Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction

scholarly journals Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 157 ◽  
Author(s):  
Pei Zhang ◽  
Yanling Wang ◽  
Likai Liang ◽  
Xing Li ◽  
Qingtian Duan
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Linear Regression Method ◽  
Power Prediction ◽  
Short Term ◽  
Outlier Identification ◽  
Dbscan Algorithm ◽  
Wind Power Prediction

Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction.

Short-term wind power prediction based on improved small-world neural network

2018 ◽  
Vol 31 (7) ◽  
pp. 3173-3185 ◽  
Author(s):  
Shuang-Xin Wang ◽  
Meng Li ◽  
Long Zhao ◽  
Chen Jin
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Small World ◽  
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.

Wavelet Neural Network Model for Short-Term Wind Power Forecasting Based on Particle Swarm Optimization Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 806-812 ◽  
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important to the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network is proposed. At the same time in order to overcome the disadvantages of the wavelet neural network for only use error reverse transmission as a fixed rule, this paper puts forward using Particle Swarm Optimization algorithm to replace the traditional gradient descent method training wavelet neural network. Through the analysis of the measured data of a wind farm, Shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

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.

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