Journal
GEOCARTO INTERNATIONAL
Volume 38, Issue 1, Pages -Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2023.2253203
Keywords
Climate factors; hydroelectric generation; hybrid deep CNN-SVR model; energy prediction
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This study utilized a unique meteorological dataset and employed a deep hybrid Convolutional Neural Network-Support Vector Regression approach to predict hydropower generation. The comparison results showed that the CNN-SVR model outperformed other models in predicting the net head and hydroelectric power generation.
This study, which aims to make predictions using a previously unused deep hybrid Convolutional Neural Network-Support Vector Regression approach for hydropower generation, was carried out using a unique meteorological data set as input data. The unique dataset was collected from Kaman Meteorology Directorate and Hirfanli HEPP in Turkey, from 2007 to 2021, to estimate the HEPP's Net Head and Hydroelectric Power Generation on a daily basis. The performances of the prediction models were benchmarked in addition to the used CNN-SVR model with Machine Learning (ML) models (Boosting Random Forest Regression (BRFR) and Weighted K-Nearest Neighbor Regression (WKNNR)) and Deep Learning (DL) models (Long-Short Term Memory (LSTM) and Deep Belief Network (DBN)). The comparison results of the used CNN-SVR model with other alternative models showed that CNN-SVR performed effectively with the highest correlation coefficient of 0.971 for NH and 0.968 for PP.
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