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

• 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
• Institution: Universiti Teknologi Petronas

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.