Combining simulate anneal algorithm with support vector regression to forecast wind speed

2010 ◽  
Author(s):  
Tang Hui ◽  
Niu Dongxiao
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Support Vector

scholarly journals Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jianzhou Wang ◽  
Qingping Zhou ◽  
Haiyan Jiang ◽  
Ru Hou
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Wind Farm ◽  
Hypothesis Test ◽  
Recurrence Plot ◽  
Support Vector ◽  
Short Term ◽  
Cuckoo Optimization Algorithm ◽  
Input Variables

This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP) and optimized support vector regression (SVR). Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA), particle swarm optimization algorithm (PSO), and cuckoo optimization algorithm (COA). Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1) analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2) the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3) the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.

Particle Swarm Optimization-Enhanced Twin Support Vector Regression for Wind Speed Forecasting

2017 ◽  
Vol 28 (5) ◽  
pp. 905-914 ◽  
Author(s):  
Essam H. Houssein
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Support Vector Regression ◽  
Renewable Energy Sources ◽  
Particle Swarm ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Swarm Optimization ◽  
Suggested Approach ◽  
Forecasting Accuracy

Abstract Wind energy is considered one of the renewable energy sources that minimize the cost of electricity production. This article proposes a hybrid approach based on particle swarm optimization (PSO) and twin support vector regression (TSVR) for forecasting wind speed (PSO-TSVR). To enhance the forecasting accuracy, TSVR was utilized to forecast the wind speed, and the optimal settings of TSVR parameters were optimized by PSO carefully. Moreover, to estimate the performance of the suggested approach, three wind speed benchmark data of OpenEI were used as a case study. The experimental results revealed that the optimized PSO-TSVR approach is able to forecast wind speed with an accuracy of 98.9%. Further, the PSO-TSVR approach has been compared with two well-known algorithms such as genetic algorithm with TSVR and the native TSVR using radial basis kernel function. The computational results proved that the proposed approach achieved better forecasting accuracy and outperformed the comparison algorithms.

Research on Multi-Step Prediction of Wind Speed Time Series Based on Parallel Support Vector Regression

2011 ◽  
Vol 347-353 ◽  
pp. 2409-2412
Author(s):  
Xiao Hong Yang ◽  
Xiao Jing Yang ◽  
Xiao Xun Zhu ◽  
Wei Xuan Xu ◽  
Kuang Ru Hai
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Wind Speed ◽  
Support Vector Regression ◽  
Computation Time ◽  
New Method ◽  
Support Vector ◽  
Prediction Result ◽  
Accuracy Of Prediction ◽  
Accumulated Error

Aiming at the problem of traditional iterative way to achieve method of multi-step prediction, new method of multi-step prediction based on parallel Support Vector Regression (SVR) was proposed. To begin with the time delay of time series will be calculated in this method, resample the time series according to the interval of time delay. What’s more the time series will be classified into several sets of data, and it sets up SVR model for the sets of each. Finally, the parallel prediction of each set is composed to get multi-step prediction result. This method not only eliminates the accumulated error, improves the accuracy of prediction, but also saves the computation time.

Noise model based ν-support vector regression with its application to short-term wind speed forecasting

2014 ◽  
Vol 57 ◽  
pp. 1-11 ◽  
Author(s):  
Qinghua Hu ◽  
Shiguang Zhang ◽  
Zongxia Xie ◽  
Jusheng Mi ◽  
Jie Wan
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Noise Model ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Model Based
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