One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach

Accurate energy production forecasting is critical when planning energy for the economic development of a country. A deep learning approach based on Long ShortTerm Memory (LSTM) to forecast onedayahead energy production from the runofriver hydroelectric power plants in Turkey was introduced in the present study. In addition to the LSTM network, three different datadriven methods, namely, adaptive neurofuzzy inference system (ANFIS) with fuzzy cmeans (FCM), ANFIS with subtractive clustering (SC), and ANFIS with grid partition (GP) were applied. The correlation coefficient (R), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used as quality metrics for prediction. Predicted values of the LSTM, ANFISFCM, ANFISSC, and ANFISGP models were compared with observed values by evaluating their errors. MAPE values in the testing process are 5.98%, 6.14%, 6.16%, and 6.40% for the LSTM neural network, ANFISFCM, ANFISSC, and ANFISGP models, respectively. The comparison revealed that the LSTM neural network provided high accuracy results in one dayahead shortterm energy production prediction and gave higher performance than other ANFIS models used in the study.