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    Artificial Neural Networks

    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

    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.

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Artificial Neural Networks

    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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    Title:

    Artificial Neural Networks

    Contributors:

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

    Published in:

    Energy Optimization and Prediction in Office Buildings ; 65-78

    SpringerBriefs in Energy

    Publication date:

    2018-01-01

    Size:

    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

    Type of media:

    Article/Chapter (Book)

    Type of material:

    Electronic Resource

    Language:

    English

    Keywords:

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

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