TY  - BOOK
AV  - none
UR  - 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
PB  - Elsevier
TI  - Prediction of energy generation target of hydropower plants using artificial neural networks
Y1  - 2022///
EP  - 320
SP  - 309
A1  - Kumar, K.
A1  - Saini, G.
A1  - Kumar, N.
A1  - Kaiser, M.S.
A1  - Kannan, R.
A1  - Shah, R.
N1  - cited By 9
SN  - 9780323912280; 9780323914284
ID  - scholars17548
N2  - 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.
ER  -