Data Driven Model for Short Term PV Power Forecasting using Least Square Support Vector Regression

2017 ◽  
Author(s):  
Ayoub Fentis ◽  
Lhoussine Bahatti ◽  
Mohamed Tabaa ◽  
Brahim Chouri ◽  
Mohammed Mestari
Keyword(s):  
Support Vector Regression ◽  
Least Square ◽  
Data Driven ◽  
Support Vector ◽  
Short Term ◽  
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.

scholarly journals Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hong Zhang ◽  
Lixing Chen ◽  
Yong Qu ◽  
Guo Zhao ◽  
Zhenwei Guo
Keyword(s):  
Support Vector Regression ◽  
Wind Power ◽  
Real Data ◽  
Search Method ◽  
Support Vector ◽  
Grid Search ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Grid Search Method ◽  
Power Forecasting

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.

Sign in / Sign up

Export Citation Format

Share Document