Wind Power Ramp Forecasting Based on Least-Square Support Vector Machine

2014 ◽  
Vol 535 ◽  
pp. 162-166 ◽  
Author(s):  
Di Gan ◽  
De Ping Ke
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Least Square ◽  
Support Vector ◽  
Proposed Model ◽  
Ramp Events ◽  
Definition Of ◽  
Short Time

Wind power ramp forecasting is very significant for grid integration of large wind energy. A ramp event is defined as the sharp increase or decrease of wind power on a large scale in short time. A methodology for wind power ramp forecasting is described. The method is based on Least Square Support Vector Machine (LSSVM) and the definition of ramp events by filtering the original signal. The performance of the proposed model is evaluated on a wind farm in China, which shows that LSSVM model is competent in forecasting wind power ramp events.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

Research of Aggregated Wind Power Prediction Method for a Region

2013 ◽  
Vol 860-863 ◽  
pp. 262-266
Author(s):  
Jin Yao Zhu ◽  
Jing Ru Yan ◽  
Xue Shen ◽  
Ran Li
Keyword(s):  
Power Systems ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Prediction Method ◽  
Support Vector ◽  
Power Prediction ◽  
Power Characteristics ◽  
Wind Power Prediction ◽  
Grey Correlation Theory

Wind power is intermittent and volatility. Some new problems would arise to power system operation when Large-scale wind farm is connected with power systems. One of the most important effect is the influence on the grid dispatch. An aggregated wind power prediction method for a region is presented. By means of analyzing power characteristics and correlation, then the greater correlation is selected as model input. Based on grey correlation theory, a least squares support vector machine prediction model is established. Finally, this method is executed on a real case and integrated wind power prediction method can effectively improve the prediction accuracy and simplify the prediction step are proved.

Short-Term Wind Power Forecasting Based on Least-Square Support Vector Machine (LSSVM)

2013 ◽  
Vol 448-453 ◽  
pp. 1825-1828 ◽  
Author(s):  
Xiao Li ◽  
Xin Wang ◽  
Yi Hui Zheng ◽  
Li Xue Li ◽  
Li Dan Zhou ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Test Sample ◽  
Least Square ◽  
Support Vector ◽  
Machine Method ◽  
Wind Power Forecasting ◽  
Forecasting Method ◽  
Reasonable Choice ◽  
Power Forecasting

In order to improve the rate and accuracy of wind power forecasting, the Least-Square Support Vector Machine method (LSSVM) is presented. LSSVM adopts equality constraints and defines the least-square system as the objective function, which can simplify the forecasting method to a large extent, as well as accelerate the rate of wind power forecasting. Through the analysis of the original load data, a reasonable choice on training set and test sample set is made in the simulation. Besides, many factors, such as, the temperature, wind direction, wind speed and power previous, are taken into consideration. The result shows that LSSVM is more effective than that of SVM.

Load Forecasting by Considering Wind Power Based on Sequential Time Classification LSSVM Model

2013 ◽  
Vol 712-715 ◽  
pp. 2437-2440 ◽  
Author(s):  
Chen Jun Yang ◽  
Ai Hui Zhang ◽  
Hai Wei Lu ◽  
Gang Wu ◽  
Hai Yan Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Wind Power ◽  
Large Scale ◽  
Load Forecasting ◽  
Support Vector ◽  
Grid Connection ◽  
Large Scale Grid ◽  
Equivalent Load ◽  
Scale Grid

In recent years, with the large-scale grid connection of wind power, wind power as an important factor to load forecasting should not be overlooked; A least squares-support vector machine (LSSVM) has been improved for the region including wind power, based on the influence from the load caused by the changes of wind and the characteristics between load and wind power. The method uses the models of least squares-support vector machine to classify and build different models , and gets the integration of each model for equivalent load forecasting, which provides the reference for the region including wind power.

scholarly journals A Bearing Fault Diagnosis Using a Support Vector Machine Optimised by the Self-Regulating Particle Swarm

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yerui Fan ◽  
Chao Zhang ◽  
Yu Xue ◽  
Jianguo Wang ◽  
Fengshou Gu
Keyword(s):  
Support Vector Machine ◽  
High Performance ◽  
Particle Swarm ◽  
Rolling Bearing ◽  
Least Square ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Novel Model

In this paper, a novel model for fault detection of rolling bearing is proposed. It is based on a high-performance support vector machine (SVM) that is developed with a multifeature fusion and self-regulating particle swarm optimization (SRPSO). The fundamental of multikernel least square support vector machine (MK-LS-SVM) is overviewed to identify a classifier that allows multidimension features from empirical mode decomposition (EMD) to be fused with high generalization property. Then the multidimension parameters of the MK-LS-SVM are configured by the SRPSO for further performance improvement. Finally, the proposed model is evaluated through experiments and comparative studies. The results prove its effectiveness in detecting and classifying bearing faults.

Wind Power Prediction Based on Similar Day Clustering Support Vector Machine

2012 ◽  
Vol 621 ◽  
pp. 200-205
Author(s):  
Xian Bin Wang ◽  
Zhi Yong Ding ◽  
Ping Yang ◽  
Xi Yang
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Wind Farm ◽  
Training Data ◽  
Support Vector ◽  
Power Prediction ◽  
Statistic Modeling ◽  
Wind Power Prediction ◽  
The Common ◽  
Similar Day

Wind power prediction technology is important to improve the reliability of grid-connected, the common statistic modeling method result is not satisfied because it lacks of effective pretreatment. This paper puts forward wind power prediction based on similar day clustering support vector machine, which catches the training data by similar day and modeling respectively, each model is used to predict specific similar days. Experiment on a wind farm shows the proposed method is effective.

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