A Data Mining-Based Wind Power Forecasting Method: Results for Wind Power Plants in Turkey

2013 ◽  
pp. 268-276 ◽  
Author(s):  
Mehmet Barış Özkan ◽  
Dilek Küçük ◽  
Erman Terciyanlı ◽  
Serkan Buhan ◽  
Turan Demirci ◽  
...  
Keyword(s):  
Data Mining ◽  
Wind Power ◽  
Power Plants ◽  
Wind Power Forecasting ◽  
Wind Power Plants ◽  
Forecasting Method ◽  
Power Forecasting

Short-Term Wind Power Forecasting in Wind Power Plants of North China Based on Support Vector Regression Analysis

2014 ◽  
Vol 705 ◽  
pp. 284-288
Author(s):  
Hai Jian Shao ◽  
Hai Kun Wei
Keyword(s):  
Support Vector Regression ◽  
Wind Power ◽  
Power Plants ◽  
Heuristic Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Wind Power Plants ◽  
Power Forecasting

This paper investigates the short-term wind power forecasting and demonstrates accurate modeling, which utilizes two representative heuristic algorithms (i.e. wavelet neural network (WNN) and Multilayer Perceptron (MLP)), and statistical machine learning techniques (i.e. Support Vector Regression (SVR)). The proposed method generates the performances of different approaches for random time series, characterized with high accuracy and high generalization capability. The employed data is obtained through Sampling equipment in Real Wind Power Plants (Power generation equipment is Dongfang Steam Turbine Co., Ltd. weak wind turbine type--FD77 with German REpower company technology). The main innovation of this paper comes from: (a) problem may encounter in the real application is in consideration such as corrupt, missing value and noisy data. (b) Data lag estimation are provided to investigate the data distribution and obtain the best input variables, respectively. (c) Comparison between MLP neural networks, WNN and SVR with optimized kernel parameters based on Grid-search method are provided to demonstrate the best forecasting approaches. The purpose of this paper is to provide a method with reference value for short-term wind 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.

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.

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