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primary topic: Prediction of energy generation target of hydropower plants using artificial neural networks

Prediction of energy generation target of hydropower plants using artificial neural networks
type: Book
type: Article
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 2011â��12 to 2019â��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
isbn: 97803239122809780323914284
status: peerReviewed
status: published
author list: authors
type: EPrint
type: BookEPrint
is part of: repository

Elsevier
type: Organization

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies
type: Collection

M.S. Kaiser
type: Person
given name: M.S.
family name: Kaiser

G. Saini
type: Person
given name: G.
family name: Saini

R. Kannan
type: Person
given name: R.
family name: Kannan

N. Kumar
type: Person
given name: N.
family name: Kumar

R. Shah
type: Person
given name: R.
family name: Shah

K. Kumar
type: Person
given name: K.
family name: Kumar

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primary topic: Prediction of energy generation target of hydropower plants using artificial neural networks