A robust probabilistic wind power forecasting method considering wind scenarios

2014 ◽  
Author(s):  
Jie Yan ◽  
Shuang Han ◽  
Furong Li ◽  
Yongqian Liu ◽  
Chenghong Gu
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting Method ◽  
Power Forecasting

A Wind Power Forecasting Method and Its Confidence Interval Estimation

2013 ◽  
Vol 186 (2) ◽  
pp. 52-60 ◽  
Author(s):  
Tatsuya Iizaka ◽  
Toru Jintsugawa ◽  
Hideyuki Kondo ◽  
Yosuke Nakanishi ◽  
Yoshikazu Fukuyama ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Wind Power ◽  
Interval Estimation ◽  
Wind Power Forecasting ◽  
Forecasting Method ◽  
Confidence Interval Estimation ◽  
Power Forecasting

A wind power forecasting method with confidence interval

2011 ◽  
Author(s):  
Tatsuya Iizaka ◽  
Toru Jintsugawa ◽  
Hideyuki Kondo ◽  
Yousuke Nakanishi ◽  
Yoshikazu Fukuyama ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting Method ◽  
Power Forecasting

Research on Power Flow Interface Oriented Regional Wind Power Forecasting Method

2014 ◽  
Vol 875-877 ◽  
pp. 1858-1862
Author(s):  
Yun Yu ◽  
Bo Yang ◽  
Fu Jiang Ge
Keyword(s):  
Wind Power ◽  
Power Flow ◽  
Large Scale ◽  
Wind Farm ◽  
Utilization Rate ◽  
Wind Power Forecasting ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Grid Side ◽  
Power Forecasting

Accurate regional wind power forecasting guarantees the security and economics of the power system integrated with large scale of wind power. Aiming at the gross wind power output of the whole regional grid area, existing regional wind power forecasting methods fails to characterize the locally gross output power of the wind farm aggregation forming a power flow interface with specified flow restraints. In this paper, the work flow of the power flow oriented regional wind power forecasting method based on whole-grid regional wind power forecasting methods was presented first. Then, the data preparation, data preprocessing and the mathematical description of the algorithm for our method were presented. Finally, the case study proved the feasibility and effectiveness of our method. The conclusion indicates that the method presented in this paper implements a multiple temporal and spatial scale regional win power forecasting technology, which can obviously improve the accuracy of regional wind power forecasting, relieve the pressure for the grid side and improve the utilization rate of wind power.

scholarly journals An Ultra-short-term Wind Power Forecasting Method with Special Error Distribution

2021 ◽  
Vol 1974 (1) ◽  
pp. 012010
Author(s):  
Shuheng Wei ◽  
Deping Ke ◽  
Jian Yang ◽  
Shangguang Jiang ◽  
Yu Liu ◽  
...  
Keyword(s):  
Wind Power ◽  
Error Distribution ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forecasting Method ◽  
Power Forecasting

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.

Sign in / Sign up

Export Citation Format

Share Document