Hydropower production prediction using artificial neural networks: an Ecuadorian application case Barzola Monteses, Julio Gómez Romero, Juan Fajardo Contreras, Waldo Artificial neural network Hydropower production forecasting LSTM MLP Monthly electricity production Sequence to sequence The authors kindly acknowledge the support from University of Guayaquil. Computational and physical resources were provided by ESPOL. Juan Gomez-Romero is partially supported by the University of Granada and the Spanish Ministries of Science, Innovation and Universities (TIN2017-91223- EXP) and Economy and Competitiveness (TIN2015-64776-C3-1-R). Hydropower is among the most efficient technologies to produce renewable electrical energy. Hydropower systems present multiple advantages since they provide sustainable and controllable energy. However, hydropower plants’ effectiveness is affected by multiple factors such as river/reservoir inflows, temperature, electricity price, among others. The mentioned factors make the prediction and recommendation of a station’s operational output a difficult challenge. Therefore, reliable and accurate energy production forecasts are vital and of great importance for capacity planning, scheduling, and power systems operation. This research aims to develop and apply artificial neural network (ANN) models to predict hydroelectric production in Ecuador’s short and medium term, considering historical data such as hydropower production and precipitations. For this purpose, two scenarios based on the prediction horizon have been considered, i.e., one-step and multi-step forecasted problems. Sixteen ANN structures based on multilayer perceptron (MLP), long short-term memory (LSTM), and sequence-to-sequence (seq2seq) LSTM were designed. More than 3000 models were configured, trained, and validated using a grid search algorithm based on hyperparameters. The results show that the MLP univariate and differentiated model of one-step scenario outperforms the other architectures analyzed in both scenarios. The obtained model can be an important tool for energy planning and decision-making for sustainable hydropower production. 2022-01-11T11:18:31Z 2022-01-11T11:18:31Z 2021-12-15 info:eu-repo/semantics/article Barzola-Monteses, J... [et al.]. Hydropower production prediction using artificial neural networks: an Ecuadorian application case. Neural Comput & Applic (2021). [https://doi.org/10.1007/s00521-021-06746-5] http://hdl.handle.net/10481/72305 10.1007/s00521-021-06746-5 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Springer