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.

scholarly journals Wind Power Prediction Based on Nonlinear Partial Least Square

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qian Wang ◽  
Yang Lei ◽  
Hui Cao
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Real Data ◽  
Partial Least Square ◽  
Least Square ◽  
Power Prediction ◽  
Short Term ◽  
The Real ◽  
Wind Power Prediction

Wind power prediction is important for the smart grid safe operation and scheduling, and it can improve the economic and technical penetration of wind energy. The intermittent and the randomness of wind would affect the accuracy of prediction. According to the sequence correlation between wind speed and wind power data, we propose a method for short-term wind power prediction. The proposed method adopts the wind speed in every sliding data window to obtain the continuous prediction of wind power. Then, the nonlinear partial least square is adopted to map the wind speed under the time series to wind power. The model carries the neural network as the nonlinear function to describe the inner relation, and the outputs of hidden layer nodes are the extension term of the original independent input matrix to partial least squares regression. To verify the effectiveness of the proposed algorithm, the real data of wind power with different working conditions are adopted in experiments. The proposed method, backpropagation neural network, radial basis function neural network, support vector machine, and partial least square are performed on the real data and their effectiveness is compared. The experimental results show that the proposed algorithm has higher precision, and the real power running curves also verify that the proposed method can predict the wind power in short-term effectively.

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%.

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.

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