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.
Full text not available from this repository.Abstract
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/).
| Item Type: | Article |
|---|---|
| Funders: | Universiti Tunku Abdul Rahman (UTAR) , Malaysia [UTARRPS 6251/H03]; UTARFSJPP; UTARFSJPP |
| Uncontrolled Keywords: | artificial neural network (ANN); support vector machine (SVM); hydropower generation (HPG); ARIMA; machine learning (ML) |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 24 Jun 2024 05:01 |
| Last Modified: | 24 Jun 2024 05:01 |
| URI: | http://eprints.um.edu.my/id/eprint/38311 |
Actions (login required)
 |
View Item |
