Chapter 12 - Prediction of energy generation target of hydropower plants using artificial neural networks. Kumar, K., Saini, G., Kumar, N., Kaiser, M. S., Kannan, R., & Shah, R. In Kumar, K., Rao, R. S., Kaiwartya, O., Kaiser, M. S., & Padmanaban, S., editors, Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies, pages 309–320. Elsevier, January, 2022. ![Chapter 12 - Prediction of energy generation target of hydropower plants using artificial neural networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 2 downloads 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.
@incollection{kumar_chapter_2022,
title = {Chapter 12 - {Prediction} of energy generation target of hydropower plants using artificial neural networks},
isbn = {978-0-323-91228-0},
url = {https://www.sciencedirect.com/science/article/pii/B9780323912280000057},
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.},
language = {en},
urldate = {2022-05-21},
booktitle = {Sustainable {Developments} by {Artificial} {Intelligence} and {Machine} {Learning} for {Renewable} {Energies}},
publisher = {Elsevier},
author = {Kumar, Krishna and Saini, Gaurav and Kumar, Narendra and Kaiser, M. Shamim and Kannan, Ramani and Shah, Rachna},
editor = {Kumar, Krishna and Rao, Ram Shringar and Kaiwartya, Omprakash and Kaiser, M. Shamim and Padmanaban, Sanjeevikumar},
month = jan,
year = {2022},
doi = {10.1016/B978-0-323-91228-0.00005-7},
keywords = {Artificial neural networks, Hydropower, Modeling, Renewable energy},
pages = {309--320},
}
Downloads: 2
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