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Prediction of energy generation target of hydropower plants using artificial neural networks

Hydropower is a renewable, reliable, and highly predictable source of energy. It has been used for centuries. The tariff of energy generation is divided into two parts: fixed charges and variable charges. Fixed charges are based on the availability of machinery (i.e., plant availability factor) and...

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Main Authors: Kumar, K., Saini, G., Kumar, N., Kaiser, M.S., Kannan, R., Shah, R.
Format: Book
Published: Elsevier 2022
Online Access:http://scholars.utp.edu.my/id/eprint/34109/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137451018&doi=10.1016%2fB978-0-323-91228-0.00005-7&partnerID=40&md5=cab75e198673f4062065d7cbd9d9d438
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spelling oai:scholars.utp.edu.my:341092023-01-03T07:23:07Z http://scholars.utp.edu.my/id/eprint/34109/ Prediction of energy generation target of hydropower plants using artificial neural networks Kumar, K. Saini, G. Kumar, N. Kaiser, M.S. Kannan, R. Shah, R. Hydropower is a renewable, reliable, and highly predictable source of energy. It has been used for centuries. The tariff of energy generation is divided into two parts: fixed charges and variable charges. Fixed charges are based on the availability of machinery (i.e., plant availability factor) and variable charges are based on the actual energy generation. The energy generation targets are decided by the local regulatory authorities for individual power plants. In this chapter, a scientific approach has been proposed to predict the energy generation target of individual power plants by using artificial neural networks (ANN). The yearly energy generation data of 12 hydropower plants, which are owned by UJVN Ltd., were selected. Past energy generation data from the financial year of 2011�12 to 2019�20 were utilized for the prediction. The prediction of yearly energy generation targets of individual power plants with a correction coefficient higher than 0.99 has been achieved. © 2022 Elsevier Inc. All rights reserved. Elsevier 2022 Book NonPeerReviewed Kumar, K. and Saini, G. and Kumar, N. and Kaiser, M.S. and Kannan, R. and Shah, R. (2022) Prediction of energy generation target of hydropower plants using artificial neural networks. Elsevier, pp. 309-320. ISBN 9780323912280; 9780323914284 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137451018&doi=10.1016%2fB978-0-323-91228-0.00005-7&partnerID=40&md5=cab75e198673f4062065d7cbd9d9d438 10.1016/B978-0-323-91228-0.00005-7 10.1016/B978-0-323-91228-0.00005-7 10.1016/B978-0-323-91228-0.00005-7
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Hydropower is a renewable, reliable, and highly predictable source of energy. It has been used for centuries. The tariff of energy generation is divided into two parts: fixed charges and variable charges. Fixed charges are based on the availability of machinery (i.e., plant availability factor) and variable charges are based on the actual energy generation. The energy generation targets are decided by the local regulatory authorities for individual power plants. In this chapter, a scientific approach has been proposed to predict the energy generation target of individual power plants by using artificial neural networks (ANN). The yearly energy generation data of 12 hydropower plants, which are owned by UJVN Ltd., were selected. Past energy generation data from the financial year of 2011�12 to 2019�20 were utilized for the prediction. The prediction of yearly energy generation targets of individual power plants with a correction coefficient higher than 0.99 has been achieved. © 2022 Elsevier Inc. All rights reserved.
format Book
author Kumar, K.
Saini, G.
Kumar, N.
Kaiser, M.S.
Kannan, R.
Shah, R.
spellingShingle Kumar, K.
Saini, G.
Kumar, N.
Kaiser, M.S.
Kannan, R.
Shah, R.
Prediction of energy generation target of hydropower plants using artificial neural networks
author_facet Kumar, K.
Saini, G.
Kumar, N.
Kaiser, M.S.
Kannan, R.
Shah, R.
author_sort Kumar, K.
title Prediction of energy generation target of hydropower plants using artificial neural networks
title_short Prediction of energy generation target of hydropower plants using artificial neural networks
title_full Prediction of energy generation target of hydropower plants using artificial neural networks
title_fullStr Prediction of energy generation target of hydropower plants using artificial neural networks
title_full_unstemmed Prediction of energy generation target of hydropower plants using artificial neural networks
title_sort prediction of energy generation target of hydropower plants using artificial neural networks
publisher Elsevier
publishDate 2022
url http://scholars.utp.edu.my/id/eprint/34109/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137451018&doi=10.1016%2fB978-0-323-91228-0.00005-7&partnerID=40&md5=cab75e198673f4062065d7cbd9d9d438
_version_ 1754532128255639552
score 13.222552

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