Impact of Climate Change on Hydro-Energy Potential A MCDM and Neural
This Brief presents the impact of climatic abnormalities on hydropower potential of different regions of the World. In this regard, multi-criteria decision making and neural network are used to predict the impact of the change cognitively by an index. The
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Mrinmoy Majumder Apu K. Saha
Impact of Climate Change on HydroEnergy Potential A MCDM and Neural Network Approach 123
SpringerBriefs in Energy
More information about this series at http://www.springer.com/series/8903
Mrinmoy Majumder Apu K. Saha •
Impact of Climate Change on Hydro-Energy Potential A MCDM and Neural Network Approach
123
Apu K. Saha National Institute of Technology Agartala India
Mrinmoy Majumder National Institute of Technology Agartala India
ISSN 2191-5520 SpringerBriefs in Energy ISBN 978-981-287-304-0 DOI 10.1007/978-981-287-305-7
ISSN 2191-5539
(electronic)
ISBN 978-981-287-305-7
(eBook)
Library of Congress Control Number: 2016936286 © The Author(s) 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media Singapore Pte Ltd.
Preface
The present study is an attempt to find the impact of climate change on water-based power plants. The investigation use decision-making method: Analytical Hierarchy Process (AHP) and a new variant of artificial neural networks; Group Method of Data Handling (GMDH) in this aspect. The indicator developed in this regard was applied to analyse the climatic vulnerability of six different hydropower plants from six continents. The result shows that both head and flow along with the operational cost were the three most important parameters which can influence the performance of plant and as well as get influenced by the change in the climate. The plant in the Australia was found to have the most and hydropower unit in the Africa was found to be the least vulnerable with respect to climatic abnormality. In respect to climate change under IPCC A2 and B2 scenarios also, African power plant was the least and Oceanian power plant was found to be the most vulnerable. The hydropower plants are dependent on climatic parameters like rainfall, evapotranspiration, etc. If regular pattern of clima
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