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Artificial Neural Networks

Rubio-Bellido, Carlos / Pérez-Fargallo, Alexis / Pulido-Arcas, Jesús

Abstract This chapter intends to demonstrate the performance and reliability of ANN in predicting large scale data not only for a single parameter, but for three of them (energy consumption, energy demand and CO2 emissions) in relation to a large-scale sample of buildings, with all the issues associated to them, such as the nonlinearity of problems related to building design and performance.

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Artificial Neural Networks

Rubio-Bellido, Carlos / Pérez-Fargallo, Alexis / Pulido-Arcas, Jesús

Abstract This chapter intends to demonstrate the performance and reliability of ANN in predicting large scale data not only for a single parameter, but for three of them (energy consumption, energy demand and CO2 emissions) in relation to a large-scale sample of buildings, with all the issues associated to them, such as the nonlinearity of problems related to building design and performance.

Artificial Neural Networks

Rubio-Bellido, Carlos / Pérez-Fargallo, Alexis / Pulido-Arcas, Jesús

Abstract This chapter intends to demonstrate the performance and reliability of ANN in predicting large scale data not only for a single parameter, but for three of them (energy consumption, energy demand and CO2 emissions) in relation to a large-scale sample of buildings, with all the issues associated to them, such as the nonlinearity of problems related to building design and performance.

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Dokumentinformationen

Titel:

Artificial Neural Networks

Beteiligte:

Rubio-Bellido, Carlos (Autor:in) / Pérez-Fargallo, Alexis (Autor:in) / Pulido-Arcas, Jesús (Autor:in)

Erschienen in:

Energy Optimization and Prediction in Office Buildings ; 65-78

SpringerBriefs in Energy

Erscheinungsdatum:

01.01.2018

Format / Umfang:

14 pages

ISBN:

978-3-319-90145-9 , 978-3-319-90146-6

ISSN:

2191-5539 , 2191-5520

DOI:

https://doi.org/10.1007/978-3-319-90146-6_5

Medientyp:

Aufsatz/Kapitel (Buch)

Format:

Elektronische Ressource

Sprache:

Englisch

Schlagwörter:

Energy , Sustainable Architecture/Green Buildings , Energy Efficiency , Building Construction and Design , Mathematical Models of Cognitive Processes and Neural Networks , Optimization

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