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    Öğe
    One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach
    (Sharif Univ Technology, 2022) Bilgili, M.; Keiyinci, S.; Ekinci, F.
    Accurate energy production forecasting is critical when planning energy for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to predict one-day-ahead energy production from the run-of-river hydroelectric power plants in Turkey was introduced in the present study. Furthermore, to compare the prediction accuracy, the methods of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fuzzy C-Means (FCM), ANTIS with Subtractive Clustering (SC), and ANFIS with Grid Partition (GP) were utilized. The predicted values obtained by the application of these four methods were evaluated with detected values. The correlation coefficient (R), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPS), and Root Mean Square Error (RMSE) were used as quality metrics for prediction. The comparison showed that the LSTM neural network provided higher accuracy results in short-term energy production prediction than other ANFIS models used in the study. (C) 2022 Sharif University of Technology. All rights reserved.
  • [ X ]
    Öğe
    One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach
    (Sharif Univ Technology, 2022) Bilgili, M.; Keiyinci, S.; Ekinci, F.
    Accurate energy production forecasting is critical when planning energy for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to predict one-day-ahead energy production from the run-of-river hydroelectric power plants in Turkey was introduced in the present study. Furthermore, to compare the prediction accuracy, the methods of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fuzzy C-Means (FCM), ANTIS with Subtractive Clustering (SC), and ANFIS with Grid Partition (GP) were utilized. The predicted values obtained by the application of these four methods were evaluated with detected values. The correlation coefficient (R), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPS), and Root Mean Square Error (RMSE) were used as quality metrics for prediction. The comparison showed that the LSTM neural network provided higher accuracy results in short-term energy production prediction than other ANFIS models used in the study. (C) 2022 Sharif University of Technology. All rights reserved.