eprintid: 17548
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/75/48
datestamp: 2023-12-19 03:23:55
lastmod: 2023-12-19 03:23:55
status_changed: 2023-12-19 03:08:15
type: book
metadata_visibility: show
creators_name: Kumar, K.
creators_name: Saini, G.
creators_name: Kumar, N.
creators_name: Kaiser, M.S.
creators_name: Kannan, R.
creators_name: Shah, R.
title: Prediction of energy generation target of hydropower plants using artificial neural networks
ispublished: pub
note: cited By 9
abstract: 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.
date: 2022
publisher: Elsevier
official_url: 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
id_number: 10.1016/B978-0-323-91228-0.00005-7
full_text_status: none
publication: Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies
pagerange: 309-320
refereed: TRUE
isbn: 9780323912280; 9780323914284
citation:   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