A New Real Time Forecasting Model for Wind Power

2012 ◽  
Vol 260-261 ◽  
pp. 231-235
Author(s):  
Song Xu ◽  
Shou Lun Chen
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Wind Power ◽  
High Dimension ◽  
Large Scale ◽  
Support Vector ◽  
Forecasting Model ◽  
New Model ◽  
The Real ◽  
Dimension Space

Wind power is the most large-scale development of technical and economic conditions of non-hydro renewable energy. The real time forecasting for wind power is difficult because of the wind power data has nonlinear interaction. A new real time forecasting model for wind power is established. In the model, state space reconstruction is used to transfer the original wind power time series to high dimension space. The input vector and anticipant output vector can be gained by the changed data in the high dimension space. Based on the theory of support vector machine, the real time forecasting model is established with the principle of structural risk minimization of support vector machine. The new model is used for the real time forecasting of wind power. The results prove the efficiency and validity of the new model.

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.

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.

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 An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1071 ◽  
Author(s):  
Yeojin Kim ◽  
Jin Hur
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Moving Average ◽  
Wind Farms ◽  
Ensemble Forecasting ◽  
Support Vector ◽  
Forecasting Model ◽  
Single Model ◽  
Simulation Based ◽  
Power Outputs

The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integration. Ensemble forecasting, which combines several forecasting techniques, is considered a viable alternative to conventional single-model-based forecasting for improving the forecasting accuracy. In this work, we propose the day-ahead ensemble forecasting of wind power using statistical methods. The ensemble forecasting model consists of three single forecasting approaches: autoregressive integrated moving average with exogenous variable (ARIMAX), support vector regression (SVR), and the Monte Carlo simulation-based power curve model. To apply the methodology, we conducted forecasting using the historical data of wind farms located on Jeju Island, Korea. The results were compared between a single model and an ensemble model to demonstrate the validity of the proposed method.

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
Sign in / Sign up

Export Citation Format

Share Document