Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods
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Dosyalar
Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Predicting full load electrical power output of a base load power plant is important in order to maximize the profit from the available megawatt hours. This paper examines and compares some machine learning regression methods to develop a predictive model, which can predict hourly full load electrical power output of a combined cycle power plant. The base load operation of a power plant is influenced by four main parameters, which are used as input variables in the dataset, such as ambient temperature, atmospheric pressure, relative humidity, and exhaust steam pressure. These parameters affect electrical power output, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected over a six-year period. First, based on these variables the best subset of the dataset is explored among all feature subsets in the experiments. Then, the most successful machine learning regression method is sought for predicting full load electrical power output. Thus, the best performance of the best subset, which contains A complete set of input variables, has been observed using the most successful method, which is Bagging algorithm with REPTree, with a mean absolute error of 2.818 and a Root Mean-Squared Error of 3.787. (C) 2014 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Prediction of electrical power output, Combined cycle power plants, Machine learning methods, Support Vector Regression, Gas-Turbine Performance, Ambient-Temperature, Energy-Consumption, Design, Model
Kaynak
International Journal of Electrical Power & Energy Systems
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
60