Load Forecasting Using Fixed-Size Least Squares Support Vector Machines

2005 ◽  
pp. 1018-1026 ◽  
Author(s):  
Marcelo Espinoza ◽  
Johan A. K. Suykens ◽  
Bart De Moor
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Load Forecasting ◽  
Support Vector ◽  
Fixed Size ◽  
Vector Machines

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.

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