Parameter selection for Support Vector Machines based on Cross Validation to short-term power load forecasting

2012 ◽  
Author(s):  
Long Yu ◽  
Yihui Zheng ◽  
Lixue Li ◽  
Lidan Zhou ◽  
Xin Wang ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Cross Validation ◽  
Load Forecasting ◽  
Parameter Selection ◽  
Support Vector ◽  
Short Term ◽  
Power Load ◽  
Vector Machines ◽  
Power Load Forecasting ◽  
Selection For

scholarly journals Dual possibilistic regression models of support vector machines and application in power load forecasting

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092163
Author(s):  
Xianfei Yang ◽  
Xiang Yu ◽  
Hui Lu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Quadratic Programming ◽  
Regression Models ◽  
Load Forecasting ◽  
Interval Data ◽  
Support Vector ◽  
Power Load ◽  
Vector Machines ◽  
Power Load Forecasting

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

scholarly journals Application of least squares support vector machines in medium-term load forecasting

2021 ◽  
Author(s):  
Mohammadreza Afshin
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Cross Validation ◽  
Load Forecasting ◽  
Support Vector ◽  
Evidence Framework ◽  
Power Load ◽  
Vector Machines ◽  
Bayesian Evidence ◽  
Bayesian Evidence Framework

Power load forecasting is essential in the task scheduling of every electricity production and distribution facility. In this project, we study the applications of modern artificial intelligence techniques in power load forecasting. We first investigate the application of principal component analysis (PCA) to least squares support vector machines (LS-SVM) in a week-ahead load forecasting problem. Then, we study a variety of tuning techniques for optimizing the least squares support vector machines' (LS-SVM) hyper-parameters. The construction of any effective and accurate LS-SVM model depends on carefully setting the associated hyper-parameters. Poplular optimization techniques including Genetic Algorithm (GA), Simulated Annealing (SA), Bayesian Evidence Framework and Cross Validation (CV) are applied to the target application and then compared for performance time, accuracy and computational cost. Analysis of the experimental results proves that LS-SVM by feature extraction using PCA can achieve greater accuracy and faster speed than other models including LS-SVM without feature extraction and the popular feed forward neural network (FFNN). Also, it is observed that optimized LS-SVM by Bayesian Evidence Framework can achieve greater accuracy and faster speed than other techniques including LS-SVM tuned with genetic algorithm, simulated annealing and 10-fold cross validation.

scholarly journals Application of least squares support vector machines in medium-term load forecasting

2021 ◽  
Author(s):  
Mohammadreza Afshin
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Cross Validation ◽  
Load Forecasting ◽  
Support Vector ◽  
Evidence Framework ◽  
Power Load ◽  
Vector Machines ◽  
Bayesian Evidence ◽  
Bayesian Evidence Framework

Power load forecasting is essential in the task scheduling of every electricity production and distribution facility. In this project, we study the applications of modern artificial intelligence techniques in power load forecasting. We first investigate the application of principal component analysis (PCA) to least squares support vector machines (LS-SVM) in a week-ahead load forecasting problem. Then, we study a variety of tuning techniques for optimizing the least squares support vector machines' (LS-SVM) hyper-parameters. The construction of any effective and accurate LS-SVM model depends on carefully setting the associated hyper-parameters. Poplular optimization techniques including Genetic Algorithm (GA), Simulated Annealing (SA), Bayesian Evidence Framework and Cross Validation (CV) are applied to the target application and then compared for performance time, accuracy and computational cost. Analysis of the experimental results proves that LS-SVM by feature extraction using PCA can achieve greater accuracy and faster speed than other models including LS-SVM without feature extraction and the popular feed forward neural network (FFNN). Also, it is observed that optimized LS-SVM by Bayesian Evidence Framework can achieve greater accuracy and faster speed than other techniques including LS-SVM tuned with genetic algorithm, simulated annealing and 10-fold cross validation.

Sign in / Sign up

Export Citation Format

Share Document