One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach
نویسندگان
چکیده
Accurate energy production forecasting is critical when planning for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to forecast one-day-ahead from run-of-river hydroelectric power plants in Turkey was introduced present study. In addition LSTM network, three different data-driven methods, namely, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM), ANFIS subtractive clustering (SC), and grid partition (GP) were applied. The correlation coefficient (R), mean absolute error (MAE), percentage (MAPE), root square (RMSE) used as quality metrics prediction. Predicted values LSTM, ANFIS-FCM, ANFIS-SC, ANFIS-GP models compared observed by evaluating their errors. MAPE testing process are 5.98%, 6.14%, 6.16%, 6.40% neural models, respectively. comparison revealed that network provided high accuracy results one day-ahead short-term prediction gave higher performance than other
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ژورنال
عنوان ژورنال: Scientia Iranica
سال: 2022
ISSN: ['1026-3098', '2345-3605']
DOI: https://doi.org/10.24200/sci.2022.58636.5825