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.

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.

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.

Hybrid Corrected Approach for Wind Power Forecasting Based on Ordinary Least Square Method

2013 ◽  
Vol 846-847 ◽  
pp. 1392-1397
Author(s):  
Hong Ying Yang ◽  
Shuang Lei Feng ◽  
Bo Wang ◽  
Wei Sheng Wang ◽  
Chun Liu
Keyword(s):  
Wind Power ◽  
Least Square Method ◽  
Least Square ◽  
Experimental Results ◽  
Correction Model ◽  
Wind Power Forecasting ◽  
Ordinary Least Square ◽  
Forecasting Method ◽  
Forecasting Errors ◽  
Power Forecasting

This paper shows an application of Ordinary Least Square (OLS) in the wind power forecasting field. The OLS algorithm is applied to obtain the estimated parameter of the hybrid correction model, and then the properly structured correction model was used to correct the forecasting errors form the physical forecasting method and the statistical forecasting method. Satisfactory experimental results are obtained for day-ahead forecast by using actual wind power data.

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