A Short-Term Wind Power Forecasting Method Based on Hybrid-Kernel Least-Squares Support Vector Machine

2021 ◽  
pp. 395-411
Author(s):  
Min Ding ◽  
Min Wu ◽  
Ryuichi Yokoyama ◽  
Yosuke Nakanishi ◽  
Yicheng Zhou
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Wind Power ◽  
Support Vector ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Forecasting Method ◽  
Hybrid Kernel ◽  
Power Forecasting

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.

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.

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.

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