@misc{10481/87119,
year = {2018},
month = {12},
url = {https://hdl.handle.net/10481/87119},
abstract = {In our state-of-the-art study, we improve neural network-based models for predicting energy consumption in buildings by parallelizing the CHC adaptive search algorithm. We compared the sequential implementation of the evolutionary algorithm with the new parallel version to obtain predictors and found that this new version of our software tool halved the execution time of the sequential version. New predictors based on various classes of neural networks have been developed and the obtained results support the validity of the proposed approaches with an average improvement of 75% of the average execution time in relation to previous sequential implementations.},
organization = {TIN201564776-C3-1-R},
organization = {TIC111},
publisher = {Elsevier},
keywords = {Energy efficiency},
keywords = {Neural networks},
keywords = {Time series prediction},
keywords = {Evolutionary algorithms},
keywords = {Manager–worker parallelization algorithms},
title = {Parallel memetic algorithm for training recurrent neural networks for the energy efficiency problem},
doi = {10.1016/j.asoc.2018.12.028},
author = {Baca Ruiz, Luis Gonzaga and Capel Tuñón, Manuel Isidoro and Pegalajar Jiménez, María Del Carmen},
}