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 ORCID: 0000-0002-4683-724X and El-Shafie, Ahmed
(2023) Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China. AIN SHAMS ENGINEERING JOURNAL, 14 (4). p. 101919.

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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 classical 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 scenario 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.

Item Type: Article
Uncontrolled Keywords: artificial neural network (ANN), support vector machine (SVM), hydropower generation (HPG), ARIMA, machine learning (ML)
Divisions: Faculty of Science and Engineering > School of Environmental Sciences
Depositing User: Symplectic Admin
Date Deposited: 12 Sep 2022 13:09
Last Modified: 18 Feb 2023 01:29
DOI: 10.1016/j.asej.2022.101919
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