publication . Article . 2014

Modeling and Estimating of Load Demand of Electricity Generated from Hydroelectric Power Plants in Turkey using Machine Learning Methods

B. DURSUN; F. AYDIN; M. ZONTUL; S. SENER;
Open Access English
  • Published: 01 Feb 2014 Journal: Advances in Electrical and Computer Engineering (issn: 1582-7445, eissn: 1844-7600, Copyright policy)
  • Publisher: Stefan cel Mare University of Suceava
Abstract
In this study, the electricity load demand, between 2012 and 2021, has been estimated using the load demand of the electricity generated from hydroelectric power plants in Turkey between 1970 and 2011. Among machine learning algorithms, Multilayer Perceptron, Locally Weighted Learning, Additive Regression, M5Rules and ZeroR classifiers are used to estimate the electricity load demand. Among them, M5Rules and Multilayer Perceptron classifiers are observed to have better performance than the others. ZeroR classifier is a kind of majority classifier used to compare the performances of other classifiers. Locally Weighted Learning and Additive Regression classifiers ...
Subjects
arXiv: Computer Science::Neural and Evolutionary Computation
free text keywords: electricity load forecasting, machine learning, multilayer perceptron, rule based learning, time series prediction, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer engineering. Computer hardware, TK7885-7895, Electrical and Electronic Engineering, General Computer Science, Engineering, business.industry, business, Electricity, Time series, Simulation, Base load power plant, Hydroelectricity, Control engineering, Automotive engineering
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