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Your search keyword '"Belany, Pavol"' showing total 24 results
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24 results on '"Belany, Pavol"'

1. Impact of Pellet Production on Power Quality: A Measurement Study

Author

Belany, Pavol, Florkova, Zuzana, Hrabovsky, Peter, Pastorkova, Jana, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Machado, Jose, editor, Soares, Filomena, editor, Antosz, Katarzyna, editor, Cagáňová, Dagmar, editor, Manupati, Vijaya Kumar, editor, and Pereira, Alejandro, editor

Published
2024
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2. Energy Efficiency Analysis of Oxide Layer Production by Plasma Electrolytic Oxidation

Author

Belany, Pavol, Florkova, Zuzana, Hrabovsky, Peter, Pastorkova, Jana, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Machado, Jose, editor, Soares, Filomena, editor, Ottaviano, Erika, editor, Valášek, Petr, editor, Reddy D., Mallikarjuna, editor, Perondi, Eduardo André, editor, and Basova, Yevheniia, editor

Published
2024
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7. A Comparative Analysis of Polynomial Regression and Artificial Neural Networks for Prediction of Lighting Consumption.

Author

Belany, Pavol, Hrabovsky, Peter, Sedivy, Stefan, Cajova Kantova, Nikola, and Florkova, Zuzana

Subjects
ARTIFICIAL neural networks, STANDARD deviations, ENERGY consumption of buildings, SUPPORT vector machines, FORECASTING, REGRESSION analysis
Abstract

This article presents a comparative analysis of two prominent machine learning techniques for predicting electricity consumption in workplace lighting systems: polynomial regression analysis and artificial neural networks. The primary objective is to assess their suitability and applicability for developing an accurate predictive model. After a brief overview of the current state of energy-saving techniques, the article examines several established models for predicting energy consumption in buildings and systems. These models include artificial neural networks, regression analysis and support vector machines. It then focuses on a practical comparison between polynomial regression analysis and an artificial neural network-based model. The article then looks at the data preparation process, outlining how the data is used within each model to establish appropriate prediction functions. Finally, it describes the methods used to evaluate the accuracy of the developed prediction functions. These functions allow the prediction of lighting consumption based on external lighting intensity. The article evaluates the accuracy of the developed prediction functions using the root mean square error, correlation coefficient and coefficient of determination values. The article compares these values obtained for both models, allowing a conclusive assessment of which model provides superior accuracy in predicting lighting consumption based on external lighting intensity. [ABSTRACT FROM AUTHOR]

Published
2024
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