Evaluation of hybrid forecasting methods for organic Rankine cycle: Unsupervised learning-based outlier removal and partial mutual information-based feature selection

•The nonlinear characteristics of real organic Rankine cycle (ORC) data are analysed.•An unsupervised learning-based algorithm is proposed for outlier removal.•A partial mutual information-based feature selection is performed.•Our hybrid method has superior performance in ORC forecasting. The constr...

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Vydané v:	Applied energy Ročník 311; s. 118682
Hlavní autori:	Ping, Xu, Yang, Fubin, Zhang, Hongguang, Xing, Chengda, Zhang, Wujie, Wang, Yan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.04.2022
Predmet:	algorithms energy Feature selection mathematical theory neural networks Organic Rankine cycle Outlier removal Partial mutual information prediction thermodynamics Unsupervised learning Partial mutual information Outlier removal Feature selection Organic Rankine cycle Unsupervised learning
ISSN:	0306-2619
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