Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms
By Ping-Feng Pai, Wei‐Chiang Hong. 2005
397 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/c7e881e8e5732553c929f648004f6ba6e8d4471c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Ravi Sankar
- Cheng-Hua Wang
- Shih-Wei Lin
- Nicholas I. Sapankevych
- Shih-Chieh Chen
- Tsung-Yuan Tseng
- Zne-Jung Lee
- Kuan-Yu Chen
In Electrical and Electronic Engineering
In Management Science and Operations Research
- Ravi Sankar
- Cheng-Hua Wang
- Shih-Wei Lin
- Nicholas I. Sapankevych
- Shih-Chieh Chen
- Tsung-Yuan Tseng
- Zne-Jung Lee
- Kuan-Yu Chen
Citations in the corpus
- Time Series Prediction Using Support Vector Machines: A Survey (2009, methodology): “Pai and Hong [65] proposed a Recurrent Support Vector Machine with Genetic Algorithms (RSVMG) for the forecasting of electrical loads.”
- Parameter determination of support vector machine and feature selection using simulated annealing approach (2008, methodology): “Their model imitates chromosome coding in their genetic algorithms to generate a set of parameter values for SVM. Pai and Hong [24] [25] also presented an SA approach to obtain parameter values for SVM and applied it to real data.”
- Support vector regression with genetic algorithms in forecasting tourism demand (2007, background): “Pai and Hong (2005) developed a recurrent SVMs with GAs to forecast electricity load.”