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

Kumar, Krishna LU orcid ; Saini, Gaurav ; Kumar, Narendra ; Kaiser, M. Shamim ; Kannan, Ramani and Shah, Rachna (2022) p.309-320
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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Artificial neural networks, Hydropower, Modeling, Renewable energy
host publication
Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies
editor
Kumar, Krishna ; Rao, Ram Shringar ; Kaiwartya, Omprakash ; Kaiser, M. Shamim and Padmanaban, Sanjeevikumar
pages
12 pages
publisher
ScienceDirect, Elsevier
external identifiers
  • scopus:85137451018
ISBN
9780323912280
9780323914284
DOI
10.1016/B978-0-323-91228-0.00005-7
language
English
LU publication?
no
additional info
Publisher Copyright: © 2022 Elsevier Inc. All rights reserved.
id
89a777c7-8ff9-4d4e-b953-d66651824a42
date added to LUP
2024-04-15 13:13:09
date last changed
2024-06-10 19:06:30
@inbook{89a777c7-8ff9-4d4e-b953-d66651824a42,
  abstract     = {{<p>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.</p>}},
  author       = {{Kumar, Krishna and Saini, Gaurav and Kumar, Narendra and Kaiser, M. Shamim and Kannan, Ramani and Shah, Rachna}},
  booktitle    = {{Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies}},
  editor       = {{Kumar, Krishna and Rao, Ram Shringar and Kaiwartya, Omprakash and Kaiser, M. Shamim and Padmanaban, Sanjeevikumar}},
  isbn         = {{9780323912280}},
  keywords     = {{Artificial neural networks; Hydropower; Modeling; Renewable energy}},
  language     = {{eng}},
  month        = {{01}},
  pages        = {{309--320}},
  publisher    = {{ScienceDirect, Elsevier}},
  title        = {{Prediction of energy generation target of hydropower plants using artificial neural networks}},
  url          = {{http://dx.doi.org/10.1016/B978-0-323-91228-0.00005-7}},
  doi          = {{10.1016/B978-0-323-91228-0.00005-7}},
  year         = {{2022}},
}