@misc{10481/72305,
year = {2021},
month = {12},
url = {http://hdl.handle.net/10481/72305},
abstract = {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.},
organization = {University of Guayaquil},
organization = {University of Granada},
organization = {Spanish Government	TIN2017-91223- EXP	
TIN2015-64776-C3-1-R},
publisher = {Springer},
keywords = {Artificial neural network},
keywords = {Hydropower production forecasting},
keywords = {LSTM},
keywords = {MLP},
keywords = {Monthly electricity production},
keywords = {Sequence to sequence},
title = {Hydropower production prediction using artificial neural networks: an Ecuadorian application case},
doi = {10.1007/s00521-021-06746-5},
author = {Barzola Monteses, Julio and Gómez Romero, Juan and Fajardo Contreras, Waldo},
}