A hybrid wind speed forecasting model using complete ensemble empirical decomposition with adaptive noise and convolutional support vector machine

2021 ◽  
Author(s):  
Vishalteja Kosana ◽  
Kiran Teeparthi ◽  
M. Santhosh
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Support Vector ◽  
Forecasting Model ◽  
Wind Speed Forecasting ◽  
Adaptive Noise

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 Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6319
Author(s):  
Chia-Sheng Tu ◽  
Chih-Ming Hong ◽  
Hsi-Shan Huang ◽  
Chiung-Hsing Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Regression Model ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Data Regression ◽  
Power Forecasting

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

scholarly journals Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Chen Wang ◽  
Jie Wu ◽  
Jianzhou Wang ◽  
Zhongjin Hu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Parameter Optimization ◽  
Cuckoo Search ◽  
Support Vector ◽  
Optimization Approach ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Comparison Results

Power systems could be at risk when the power-grid collapse accident occurs. As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power. Therefore, accurate wind power and wind speed forecasting are in need. In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I) data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD), which reduces the effect of noise on the wind speed data; (II) artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM) model are optimized by the cuckoo search (CS) algorithm; (III) parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD) method is proposed. The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.

scholarly journals Ultra-short-term wind speed forecasting based on support vector machine with combined kernel function and similar data

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jian He ◽  
Jingle Xu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Kernel Functions ◽  
Support Vector ◽  
Similar Data ◽  
Power Prediction ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Power Prediction

Abstract The accuracy of wind power prediction is very important for the stable operation of a power system. Ultra-short-term wind speed forecasting is an effective way to ensure real-time and accurate wind power prediction. In this paper, a short-term wind speed forecasting method based on a support vector machine with a combined kernel function and similar data is proposed. Similar training data are selected based on the wind tendency, and a combination of two kinds of kernel functions is applied in forecasting using a support vector machine. The forecasting results for a wind farm in Ningxia Province indicate that a combination of kernel functions with complementary advantages outperforms each single function, and forecasting models based on grouped wind data with a similar tendency could reduce the forecasting error. Furthermore, more accurate wind forecasting results ensure better wind power prediction.

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 Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Wenyu Zhang ◽  
Zhongyue Su ◽  
Hongli Zhang ◽  
Yanru Zhao ◽  
Zhiyuan Zhao
Keyword(s):  
Wind Speed ◽  
Singular Spectrum Analysis ◽  
Arima Model ◽  
Wind Farms ◽  
Weather Data ◽  
Support Vector ◽  
Forecasting Model ◽  
Ann Model ◽  
Efficient Operation ◽  
Wind Speed Forecasting

Accurate wind speed forecasting is important for the reliable and efficient operation of the wind power system. The present study investigated singular spectrum analysis (SSA) with a reduced parameter algorithm in three time series models, the autoregressive integrated moving average (ARIMA) model, the support vector machine (SVM) model, and the artificial neural network (ANN) model, to forecast the wind speed in Shandong province, China. In the proposed model, the weather research and forecasting model (WRF) is first employed as a physical background to provide the elements of weather data. To reduce these noises, SSA is used to develop a self-adapting parameter selection algorithm that is fully data-driven. After optimization, the SSA-based forecasting models are applied to forecasting the immediate short-term wind speed and are adopted at ten wind farms in China. Finally, the performance of the proposed approach is evaluated using observed data according to three error calculation methods. The simulation results from ten cases show that the proposed method has better forecasting performance than the traditional methods.

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