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Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China

Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the clas-sical mathematical models. Therefore, this study investigates the capability of various m...

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Main Authors: Hanoon, Marwah Sattar, Ahmed, Ali Najah, Razzaq, Arif, Oudah, Atheer Y., Alkhayyat, Ahmed, Huang, Yuk Feng, Kumar, Pavitra, El-Shafie, Ahmed
Format: Article
Published: Elsevier 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/38311/
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spelling my.um.eprints.383112024-06-24T05:01:05Z http://eprints.um.edu.my/38311/ Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China Hanoon, Marwah Sattar Ahmed, Ali Najah Razzaq, Arif Oudah, Atheer Y. Alkhayyat, Ahmed Huang, Yuk Feng Kumar, Pavitra El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the clas-sical mathematical models. Therefore, this study investigates the capability of various machine learning algorithms in predicting the power production of a reservoir located in China using data from 1979 to 2016. In this study, different supervised and unsupervised machine learning algorithms are proposed: artificial neural network (ANN), AutoRegressive Integrated Moving Aveage (ARIMA) and support vector machine (SVM). Three different scenarios are examined, such as scenario1 (SC1): used to predict daily power generation, scenario 2 (SC2): used to predict power generation for monthly prediction and sce-nario 3 (SC3): used to predict hydropower generation (HPG) seasonally. The statistical analysis and pre-processing techniques were applied to the raw data before developing the models. Five statistical indexes are employed to evaluate the performances of various models developed. The results indicate that the proposed models can be used to predict HPG efficiently and could be an effective method for energy decision-makers. The sensitivity analyses found the most effective models for predicting HPG for three scenarios using graphical distribution data (Taylor diagram). Regarding the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models for ANN and SVM. The results presented that the value of 95PPU for all models falls into the range between 80% and 100%. As for the d-factor, all values in all scenarios are less than one. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/). Elsevier 2023-04 Article PeerReviewed Hanoon, Marwah Sattar and Ahmed, Ali Najah and Razzaq, Arif and Oudah, Atheer Y. and Alkhayyat, Ahmed and Huang, Yuk Feng and Kumar, Pavitra and El-Shafie, Ahmed (2023) Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China. Ain Shams Engineering Journal, 14 (4). ISSN 2090-4479, DOI https://doi.org/10.1016/j.asej.2022.101919 2090-4479 <https://doi.org/10.1016/j.asej.2022.101919 2090-4479>. 10.1016/j.asej.2022.101919 2090-4479
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Hanoon, Marwah Sattar
Ahmed, Ali Najah
Razzaq, Arif
Oudah, Atheer Y.
Alkhayyat, Ahmed
Huang, Yuk Feng
Kumar, Pavitra
El-Shafie, Ahmed
Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
description Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the clas-sical mathematical models. Therefore, this study investigates the capability of various machine learning algorithms in predicting the power production of a reservoir located in China using data from 1979 to 2016. In this study, different supervised and unsupervised machine learning algorithms are proposed: artificial neural network (ANN), AutoRegressive Integrated Moving Aveage (ARIMA) and support vector machine (SVM). Three different scenarios are examined, such as scenario1 (SC1): used to predict daily power generation, scenario 2 (SC2): used to predict power generation for monthly prediction and sce-nario 3 (SC3): used to predict hydropower generation (HPG) seasonally. The statistical analysis and pre-processing techniques were applied to the raw data before developing the models. Five statistical indexes are employed to evaluate the performances of various models developed. The results indicate that the proposed models can be used to predict HPG efficiently and could be an effective method for energy decision-makers. The sensitivity analyses found the most effective models for predicting HPG for three scenarios using graphical distribution data (Taylor diagram). Regarding the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models for ANN and SVM. The results presented that the value of 95PPU for all models falls into the range between 80% and 100%. As for the d-factor, all values in all scenarios are less than one. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).
format Article
author Hanoon, Marwah Sattar
Ahmed, Ali Najah
Razzaq, Arif
Oudah, Atheer Y.
Alkhayyat, Ahmed
Huang, Yuk Feng
Kumar, Pavitra
El-Shafie, Ahmed
author_facet Hanoon, Marwah Sattar
Ahmed, Ali Najah
Razzaq, Arif
Oudah, Atheer Y.
Alkhayyat, Ahmed
Huang, Yuk Feng
Kumar, Pavitra
El-Shafie, Ahmed
author_sort Hanoon, Marwah Sattar
title Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
title_short Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
title_full Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
title_fullStr Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
title_full_unstemmed Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China
title_sort prediction of hydropower generation via machine learning algorithms at three gorges dam, china
publisher Elsevier
publishDate 2023
url http://eprints.um.edu.my/38311/
_version_ 1805881104712859648
score 13.211869

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