Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques

• Κύριοι συγγραφείς: Majumder, Mrinmoy (Συγγραφέας), Saha, Apu K. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2016.
• Σειρά: SpringerBriefs in Energy,
• Θέματα: Energy.; Renewable energy resources.; Climate change.; Electric power production.; Computational intelligence.; Renewable energy sources.; Alternate energy sources.; Green energy industries.; Environmental economics.; Renewable and Green Energy.; Computational Intelligence.; Energy Technology.; Environmental Economics.; Climate Change/Climate Change Impacts.
• Φυσική περιγραφή: X, 49 p. 14 illus., 13 illus. in color. online resource.
• ISBN: 9789812873088
• ISSN: 2191-5520

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.