Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques

This Brief highlights a novel model to find out the feasibility of any location to produce solar energy. The model utilizes the latest multi-criteria decision making techniques and artificial neural networks to predict the suitability of a location to maximize allocation of available energy for prod...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Majumder, Mrinmoy (Autor), Saha, Apu K. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore : Springer Nature Singapore : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:SpringerBriefs in Energy,
Temas:
Renewable energy sources.
Computational intelligence.
Electric power production.
Environmental economics.
Climatology.
Renewable Energy.
Computational Intelligence.
Electrical Power Engineering.
Mechanical Power Engineering.
Environmental Economics.
Climate Sciences.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques  |h [electronic resource] /  |c by Mrinmoy Majumder, Apu K. Saha. 
250 |a 1st ed. 2016. 
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300 |a X, 49 p. 14 illus., 13 illus. in color.  |b online resource. 
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490 1 |a SpringerBriefs in Energy,  |x 2191-5539 
505 0 |a Introduction -- Justification -- Solar Energy -- Solar Energy -- Importance -- Benefits of Solar energy -- MCDM -- Definitions -- Applications -- Artificial Neural Network -- Definition -- Development Procedure of Models -- Development of the Feasibility Model -- Application of MCDM -- Development of Feasibility Index -- Model Validation of the Model -- Sensitivity Analysis -- Case Studies -- Locations -- Why this location ? -- Results and Discussion -- MCDM Results -- ANN Results -- Conclusion. 
520 |a This Brief highlights a novel model to find out the feasibility of any location to produce solar energy. The model utilizes the latest multi-criteria decision making techniques and artificial neural networks to predict the suitability of a location to maximize allocation of available energy for producing optimal amount of electricity which will satisfy the demand from the market. According to the results of the case studies further applications are encouraged. 
650 0 |a Renewable energy sources. 
650 0 |a Computational intelligence. 
650 0 |a Electric power production. 
650 0 |a Environmental economics. 
650 0 |a Climatology. 
650 1 4 |a Renewable Energy. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Electrical Power Engineering. 
650 2 4 |a Mechanical Power Engineering. 
650 2 4 |a Environmental Economics. 
650 2 4 |a Climate Sciences. 
700 1 |a Saha, Apu K.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
710 2 |a SpringerLink (Online service) 
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950 |a Energy (R0) (SpringerNature-43717) 

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