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 classical mathematical models. Therefore, this study investigates the capability of various ma...
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my.uniten.dspace-342452024-10-14T11:18:37Z Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China Sattar Hanoon M. Najah Ahmed A. Razzaq A. Oudah A.Y. Alkhayyat A. Feng Huang Y. kumar P. El-Shafie A. 57266877500 57214837520 57219410567 57210341575 59268596900 55807263900 57206939156 16068189400 ARIMA artificial neural network (ANN) hydropower generation (HPG) machine learning (ML) support vector machine (SVM) Decision making Forecasting Hydroelectric power Learning algorithms Neural networks Reservoirs (water) Sensitivity analysis Stochastic models Stochastic systems Uncertainty analysis Artificial neural network Auto-regressive Autoregressive integrated moving aveage Hydro-power generation Hydropower generation Machine learning Machine learning algorithms Machine-learning Support vector machine Support vectors machine Support vector machines 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. � 2022 THE AUTHORS Final 2024-10-14T03:18:37Z 2024-10-14T03:18:37Z 2023 Article 10.1016/j.asej.2022.101919 2-s2.0-85136242754 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136242754&doi=10.1016%2fj.asej.2022.101919&partnerID=40&md5=28d8bea2af06be79f2b62f64a311970d https://irepository.uniten.edu.my/handle/123456789/34245 14 4 101919 All Open Access Gold Open Access Ain Shams University Scopus |
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ARIMA artificial neural network (ANN) hydropower generation (HPG) machine learning (ML) support vector machine (SVM) Decision making Forecasting Hydroelectric power Learning algorithms Neural networks Reservoirs (water) Sensitivity analysis Stochastic models Stochastic systems Uncertainty analysis Artificial neural network Auto-regressive Autoregressive integrated moving aveage Hydro-power generation Hydropower generation Machine learning Machine learning algorithms Machine-learning Support vector machine Support vectors machine Support vector machines |
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ARIMA artificial neural network (ANN) hydropower generation (HPG) machine learning (ML) support vector machine (SVM) Decision making Forecasting Hydroelectric power Learning algorithms Neural networks Reservoirs (water) Sensitivity analysis Stochastic models Stochastic systems Uncertainty analysis Artificial neural network Auto-regressive Autoregressive integrated moving aveage Hydro-power generation Hydropower generation Machine learning Machine learning algorithms Machine-learning Support vector machine Support vectors machine Support vector machines Sattar Hanoon M. Najah Ahmed A. Razzaq A. Oudah A.Y. Alkhayyat A. Feng Huang Y. kumar P. El-Shafie A. 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 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. � 2022 THE AUTHORS |
| author2 |
57266877500 |
| author_facet |
57266877500 Sattar Hanoon M. Najah Ahmed A. Razzaq A. Oudah A.Y. Alkhayyat A. Feng Huang Y. kumar P. El-Shafie A. |
| format |
Article |
| author |
Sattar Hanoon M. Najah Ahmed A. Razzaq A. Oudah A.Y. Alkhayyat A. Feng Huang Y. kumar P. El-Shafie A. |
| author_sort |
Sattar Hanoon M. |
| 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 |
Ain Shams University |
| publishDate |
2024 |
| _version_ |
1814061110976839680 |
| score |
13.222552 |