Short-Term Wind Speed Prediction on Base of Improved Least Squares Support Vector Machine

2014 ◽  
Vol 599-601 ◽  
pp. 1972-1975
Author(s):  
Zheng Zhao ◽  
Long Xin Zhang ◽  
Hai Tao Liu ◽  
Zi Rui Liu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Wind Farm ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Neural Network Prediction ◽  
Speed Prediction

Accurate wind speed prediction is of significance to improve the ability to coordinate operation of a wind farm with a power system and ensure the safety of power grid operation. According to the randomness and volatility of wind speed, it is put forward that a WD_GA_LS_SVM short-term wind speed combination prediction model on basis of Wavelet decomposition (WD), Genetic alogorithms (GA) optimization and Least squares support vector machine (LS_SVM). Short-term wind speed prediction is carried out and compared with the neural network prediction model with use of the measured data of a wind farm. The results of error analysis indicate the combination prediction model selected is of higher prediction accuracy.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

2014 ◽  
Vol 511-512 ◽  
pp. 927-930
Author(s):  
Shuai Zhang ◽  
Hai Rui Wang ◽  
Jin Huang ◽  
He Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Speed Prediction ◽  
Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

Wind Speed Prediction Based on Improved Grey Neural Network Model

2014 ◽  
Vol 548-549 ◽  
pp. 1235-1240
Author(s):  
Bin Zeng ◽  
Jian Xiao Zou ◽  
Kai Li ◽  
Xiao Shuai Xin
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Data Storage ◽  
Wind Farm ◽  
Grey System Theory ◽  
Grey System ◽  
Wind Speed Prediction ◽  
Neural Network Prediction ◽  
Speed Prediction

Wind speed forecasting is an effective method to improve power stability of wind farm. Grey system theory have certain advantages in the study of poor information and uncertainty problems, it is suitable for the system with limited computing power and data storage capacity, such as wind turbine control system. In order to further improve the prediction accuracy of grey model, we combined GM (1, 1) model and BP neural network prediction model in this paper, and improved the combined model by background value optimizing and introducing genetic algorithm. Through analyzing the simulation results and comparing the forecasting results with the actual wind speed, it is clear that the improved combined prediction model is superior to pure grey forecasting model and it meets the needs of the wind power control.

Short-Term Wind Speed Forecasting Based on Optimizated Support Vector Machine

2013 ◽  
Vol 300-301 ◽  
pp. 189-194 ◽  
Author(s):  
Yu Sun ◽  
Ling Ling Li ◽  
Xiao Song Huang ◽  
Chao Ying Duan
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Short Term ◽  
Neural Network Prediction ◽  
Random Fluctuations ◽  
The Impact

To avoid the impact which is caused by the characteristics of the random fluctuations of the wind speed to grid-connected wind power generation system, accurately prediction of short-term wind speed is needed. This paper designed a combination prediction model which used the theories of wavelet transformation and support vector machine (SVM). This improved the model’s prediction accuracy through the method of achiving change character in wind speed sequences in different scales by wavelet transform and optimizing the parameters of support vector machines through the improved particle swarm algorithm. The model showed great generalization ability and high prediction accuracy through the experiment. The lowest root-mean-square error of 200 samples was up to 0.0932 and the model’s effect was much stronger than the BP neural network prediction model. It provided an effective method for predicting wind speed.

scholarly journals Sample Selection Based on Active Learning for Short-Term Wind Speed Prediction

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 337 ◽  
Author(s):  
Jian Yang ◽  
Xin Zhao ◽  
Haikun Wei ◽  
Kanjian Zhang
Keyword(s):  
Wind Speed ◽  
Active Learning ◽  
Wind Farm ◽  
Sample Selection ◽  
Support Vector ◽  
Short Term ◽  
Learning Methods ◽  
Wind Speed Prediction ◽  
Training Samples ◽  
Speed Prediction

Wind speed prediction is the key to wind power prediction, which is very important to guarantee the security and stability of the power system. Due to dramatic changes in wind speed, it needs high-frequency sampling to describe the wind. A large number of samples are generated and affect modeling time and accuracy. Therefore, two novel active learning methods with sample selection are proposed for short-term wind speed prediction. The main objective of active learning is to minimize the number of training samples and ensure the prediction accuracy. In order to verify the validity of the proposed methods, the results of support vector regression (SVR) and artificial neural network (ANN) models with different training sets are compared. The experimental data are from a wind farm in Jiangsu Province. The simulation results show that the two novel active learning methods can effectively select typical samples. While reducing the number of training samples, the prediction performance remains almost the same or slightly improved.

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