Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques

by Mrinmoy Majumder, Apu K. Saha

Overview

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.

Product Details

ISBN-13:	9789812873071
Publisher:	Springer Nature Singapore
Publication date:	04/30/2016
Series:	SpringerBriefs in Energy
Edition description:	1st ed. 2016
Pages:	49
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Dr. Mrinmoy Majumder is presently working as an Assistant Professor in the School of Hydro-Informatics Engineering at the National Institute of Technology, Agartala, India. He has published over 25 research papers in international journals and has published five books with international publishers.

Dr. Apu Kumar Saha is presently working as an Assistant Professor in the Department of Mathematics at the National Institute of Technology, Agartala, India. He has published several research papers in various International Journals and conferences. He has also contributed two book chapters.

Table of Contents

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.