Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine

2011 ◽  
Vol 18 (4) ◽  
pp. 1184-1192 ◽  
Author(s):  
Yong-xiu He ◽  
Hai-ying He ◽  
Yue-jin Wang ◽  
Tao Luo
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Least Squares ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Forecasting Model ◽  
General Regression

Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting

2012 ◽  
Vol 630 ◽  
pp. 366-371 ◽  
Author(s):  
Kuo Ping Lin
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Support Vector Regression ◽  
Performance Prediction ◽  
Pso Algorithm ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Linear Kernel ◽  
Performance Forecasting ◽  
General Regression

The success of CPU performance prediction will make many benefits. This study adopts the least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm to improver accuracy of CPU performance prediction. LS-SVR with PSO, support vector regression (SVR) with PSO, general regression neural network (GRNN), radial basis neural network (RBNN), and linear regression are employed for CPU performance prediction. Empirical results indicate that the LS-SVR (Linear kernel) with PSO has better performance in terms of forecasting accuracy than the other methods. Therefore, the LS-SVR (Linear kernel) with PSO model can efficiently provide credible CPU performance estimated value.

Zenith Troposphere Delay Prediction based on BP Neural Network and Least Squares Support Vector Machine

2020 ◽  
Author(s):  
Tianhe Xu ◽  
Song Li ◽  
Nan Jiang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Least Squares ◽  
Bp Neural Network ◽  
Main Trend ◽  
Support Vector ◽  
Space Domain ◽  
High Efficient ◽  
New Strategy ◽  
Delay Prediction

<p><strong>Abstract</strong><strong>:</strong> With the rapid development of artificial intelligence, machine learning has become an high-efficient tool applied in the fields of GNSS data analysis and processing, such as troposphere, ionosphere or satellite clock modeling and prediction. In this paper, zenith troposphere delay (ZTD) prediction algorithms based on BP neural network (BPNN) and least squares support vector machine (LSSVM) are proposed in the time and space domain. The main trend terms in ZTD time series are deducted by polynomial fitting, and the remaining residuals are reconstructed and modeled by BPNN and LSSVM algorithm respectively. The test results show that the performance of LSSVM is better than that of BPNN in term of prediction stability and accuracy by using ZTD products of International GNSS Service (IGS) of 20 stations in time domain. In order to further improve LSSVM prediction accuracy, a new strategy of training samples selection based on correlation analysis is proposed. The results show that using the proposed strategy, about 80% to 90% of the 1-hour prediction deviation of LSSVM can reach millimeter level depending on the season, and the percentage of the prediction deviation value less than 5 mm is about 60% to 70%, which is 5% to 20% higher than that of the classical random selection in different month. The mean values of RMSE in all 20 stations using the new strategy are 1-3mm smaller than those of the classical one. Then different prediction span from 1 to 12 hours is conducted to show the performance of the proposed method. Finally, the ZTD predictions based on BPNN and LSSVM in space domain are also verified and compared using GNSS CORS network data of Hong Kong, China.</p><p><strong>Keywords</strong><strong>:</strong> ZTD, BP Neural Network, Support Vector Machine, Least Squares, GNSS</p><p><strong>Acknowledgments:</strong> This work was supported by Natural Science Foundation of China (41874032) and the National Key Research and Development Program (2016YFB0501701)</p><p> </p>

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.

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