Forecasting Model of Irrigation Water Requirement Based on Least Squares Support Vector Machine

2010 ◽  
Author(s):  
Fang Xie ◽  
De-shan Tang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Irrigation Water ◽  
Water Requirement ◽  
Support Vector ◽  
Forecasting Model ◽  
Irrigation Water Requirement

scholarly journals Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model

2019 ◽  
Vol 11 (3) ◽  
pp. 652 ◽  
Author(s):  
Qunli Wu ◽  
Huaxing Lin
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Least Squares ◽  
Bat Algorithm ◽  
Support Vector ◽  
Forecasting Model ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Mode Decomposition

With the integration of wind energy into electricity grids, wind speed forecasting plays an important role in energy generation planning, power grid integration and turbine maintenance scheduling. This study proposes a hybrid wind speed forecasting model to enhance prediction performance. Variational mode decomposition (VMD) was applied to decompose the original wind speed series into different sub-series with various frequencies. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by a bat algorithm (BA) was established to forecast those sub-series extracted from VMD. The ultimate forecast of wind speed can be obtained by accumulating the prediction values of each sub-series. The results show that: (a) VMD-BA-LSSVM displays better capacity for the prediction of ultra short-term (15 min) and short-term (1 h) wind speed forecasting; (b) the proposed forecasting model was compared with wavelet decomposition (WD) and ensemble empirical mode decomposition (EEMD), and the results indicate that VMD has stronger decomposition ability than WD and EEMD, thus, significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.

scholarly journals An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1687
Author(s):  
Fang Yuan ◽  
Jiang Guo ◽  
Zhihuai Xiao ◽  
Bing Zeng ◽  
Wenqiang Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Phase Space ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Transformer Oil ◽  
Gas Content ◽  
Support Vector ◽  
Forecasting Model ◽  
Dissolved Gas

Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

Wind Speed Forecasting Based on Least Squares Support Vector Machine and Particle Swarm Optimization

2014 ◽  
Vol 602-605 ◽  
pp. 3251-3255
Author(s):  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Least Squares ◽  
Wind Farm ◽  
Particle Swarm ◽  
Support Vector ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Machine Theory

This paper is based on Least Squares Support Vector Machine theory to build the wind speed forecasting model. Meanwhile, as there is still no effective choice method of Least Squares Support Vector Ma-chine parameter, this paper tried to use Particle Swarm Optimization theory to optimization choice for parameter. And last, use wind farm observed wind speed (sampling interval is 1 minute) of three days to forecast the next minute wind speed through this paper's wind forecasting model, and prediction result is that the MAPE is only 4.63%, the prediction effect is relative ideal, confirm the feasibility of applying the Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine theory to forecast the wind speed, it will provide theoretical support to wind farm layout and wind power forecasting and so on.

scholarly journals A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power

Energies ◽  
2012 ◽  
Vol 5 (9) ◽  
pp. 3329-3346 ◽  
Author(s):  
Qian Zhang ◽  
Kin Keung Lai ◽  
Dongxiao Niu ◽  
Qiang Wang ◽  
Xuebin Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Model Based ◽  
Fuzzy Group

Q-learning System Based on Cooperative Least Squares Support Vector Machine

2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Learning System ◽  
Support Vector ◽  
Q Learning
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