DOIONLINE 
DOIONLINE Publish In |
International Journal of Industrial Electronics and Electrical Engineering (IJIEEE)-IJIEEE |
 Journal Home Volume Issue |
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Issue |
Volume-4,Issue-6 ( Jun, 2016 ) | |||||||||
Paper Title |
Electric Load Forecasting Using Regularized Extreme Learning Machines | |||||||||
Author Name |
Sami Ekici | |||||||||
Affilition |
Energy Systems Engineering Department 23119 ElazigTurkey | |||||||||
Pages |
119-122 | |||||||||
Abstract |
This paper presents a method based on extreme learning machine (ELM) for electric load forecasting. Recently, extreme learning machine (ELM) has been widely used in machine learning applications due to its ability for extremely fast learning. The usage of big data sets in the training gives raise to computation problems. ELM overcomes the computation problems with its higher learning speed than classical artificial neural networks (ANN). In this paper, performances of ELM and regularized ELM (RELM) are investigated for electric load forecasting. The obtained results from RELM are compared with the classical ELM method and ANN. It has been shown that RELM has fast training advantage on ANN and higher output accuracy rate than ELM. Keywords— Electric Load Forecasting, Extreme Learning Machine, Artificial Neural Network. | |||||||||
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