@article{DIVINA2020,
title = {Hybridizing deep learning and neuroevolution: Application to the Spanish short-term electric energy consumption forecasting},
author = {F. Divina and J. F. Torres and M. García-Torres and F. Martínez-Álvarez and A. Troncoso},
url = {https://www.mdpi.com/2076-3417/10/16/5487},
doi = {https://doi.org/10.3390/app10165487},
year = {2020},
date = {2020-07-30},
journal = {Applied Sciences},
volume = {10},
number = {16},
pages = {5487},
abstract = {The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{MARTINEZ-ALVAREZ20,
title = {Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model},
author = {F. Martínez-Álvarez and G. Asencio-Cortés and J. F. Torres and D. Gutiérrez-Avilés and L. Melgar-García and R. Pérez-Chacón and C. Rubio-Escudero and A. Troncoso and J. C. Riquelme},
url = {https://www.liebertpub.com/doi/full/10.1089/big.2020.0051},
doi = {10.1089/big.2020.0051},
year = {2020},
date = {2020-07-22},
journal = {Big Data},
volume = {8},
number = {4},
pages = {308-322},
abstract = {This work proposes a novel bioinspired metaheuristic, simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures or traveling rate are introduced into the model in order to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate and number of recoveries, the infected population gradually decreases. The Coronavirus Optimization Algorithm has two major advantages when compared to other similar strategies. Firstly, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Secondly, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multi-virus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{TORRES19-2,
title = {Random Hyper-Parameter Search-Based Deep Neural Network for Power Consumption Forecasting},
author = {J. F. Torres and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-030-20521-8_22},
doi = {https://doi.org/10.1007/978-3-030-20521-8_22},
year = {2019},
date = {2019-05-16},
booktitle = {IWANN 15th International Work-Conference on Artificial Neural Networks},
volume = {11506},
pages = {259-269},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@article{TORRES19-1,
title = {Big data solar power forecasting based on deep learning and multiple data sources},
author = {J. F. Torres and A. Troncoso and I. Koprinska and Z. Wang and F. Martínez-Álvarez},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12394},
doi = {https://doi.org/10.1111/exsy.12394},
year = {2019},
date = {2019-03-01},
journal = {Expert Systems},
volume = {36},
pages = {id12394},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{INFSCI2018,
title = {A novel Spark-based multi-step forecasting algorithm for big data time series},
author = {A. Galicia and J. F. Torres and F. Martínez-Álvarez and A. Troncoso},
url = {https://www.sciencedirect.com/science/article/pii/S0020025518304493},
doi = {10.1016/j.ins.2018.06.010},
year = {2018},
date = {2018-01-01},
journal = {Information Sciences},
volume = {467},
pages = {800-818},
abstract = {This paper presents different scalable methods for predicting big time series, namely time series with a high frequency measurement. Methods are also developed to deal with arbitrary prediction horizons. The Apache Spark framework is proposed for distributed computing in order to achieve the scalability of the methods. Prediction methods have been developed using Spark’s MLlib library for machine learning. Since the library does not support multivariate regression, the prediction problem is formulated as h prediction sub-problems, where h is the number of future values to predict, that is, the prediction horizon. Furthermore, different kinds of representative methods have been chosen, such as decision trees, two tree-based ensemble techniques (Gradient-Boosted and Random Forest) and a linear regression method as a reference method for comparisons. Finally, the methodology has been tested in a real time series of electrical demand in Spain, with a time interval of ten minutes between measurements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{ICAE2018,
title = {A scalable approach based on deep learning for big data time series forecasting},
author = {J. F. Torres and A. Galicia and A. Troncoso and F. Martínez-Álvarez},
url = {https://content.iospress.com/articles/integrated-computer-aided-engineering/ica580},
doi = {10.3233/ICA-180580},
year = {2018},
date = {2018-01-01},
journal = {Integrated Computer-Aided Engineering},
volume = {25},
number = {4},
pages = {335-348},
abstract = {This paper presents a method based on deep learning to deal with big data times series forecasting. The deep feed forward neural network provided by the H2O big data analysis framework has been used along with the Apache Spark platform for distributed computing. Since H2O does not allow the conduction of multi-step regression, a general-purpose methodology that can be used for prediction horizons with arbitrary length is proposed here, being the prediction horizon, h, the number of future values to be predicted. The solution consists in splitting the problem into h forecasting subproblems, being h the number of samples to be simultaneously predicted. Thus, the best prediction model for each subproblem can be obtained, making easier its parallelization and adaptation to the big data context. Moreover, a grid search is carried out to obtain the optimal hyperparameters of the deep learning-based approach. Results from a real-world dataset composed of electricity consumption in Spain, with a ten-minute frequency sampling rate, from 2007 to 2016 are reported. In particular, the accuracy and runtimes versus computing resources and size of the dataset are analyzed. Finally, the performance and the scalability of the proposed method is compared to other recently published techniques, showing to be a suitable method to process big data time series.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{SOCO2018,
title = {Deep learning for big data time series forecasting applied to solar power},
author = {J. F. Torres and A. Troncoso and I. Koprinska and Z. Wang and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-319-94120-2_12},
year = {2018},
date = {2018-01-01},
booktitle = {SOCO 13th International Conference on Soft Computing Models in Industrial and Environmental Applications},
series = {Advances in Intelligent Systems and Computing},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@article{Energy2018,
title = {Stacking ensemble learning for short-term electricity consumption forecasting},
author = {F. Divina and A. Gilson and F. Goméz-Vela and M. García-Torres and J. F. Torres},
url = {https://www.mdpi.com/1996-1073/11/4/949},
doi = {https://doi.org/10.3390/en11040949},
year = {2018},
date = {2018-01-01},
journal = {Energies},
volume = {11},
number = {4},
pages = {949},
abstract = {The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{IWINAC2017,
title = {Deep Learning - Based Approach for Time Series Forecasting with Application to Electricity Load},
author = {J. F. Torres and A. Troncoso and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-319-59773-7_21},
year = {2017},
date = {2017-01-01},
booktitle = {IWINAC International Work-Conference on the Interplay Between Natural and Artificial Computation},
series = {Lecture Notes in computer Science},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{IWANN2017,
title = {Scalable Forecasting Techniques Applied to Big Electricity Time Series},
author = {A. Galicia and J. F. Torres and F. Martínez-Álvarez and A. Troncoso},
url = {https://link.springer.com/chapter/10.1007/978-3-319-59147-6_15},
year = {2017},
date = {2017-01-01},
booktitle = {IWANN International Work-Conference on Artificial Neural Networks},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{TAMIDA2016,
title = {Automated Spark clusters deployment for Big Data with standalone applications integration},
author = {A. M. Fernández and J. F. Torres and A. Troncoso and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-319-44636-3_14},
year = {2016},
date = {2016-01-01},
booktitle = {CAEPIA Multiconferencia de la Asociación Española para la Inteligencia Artificial (TAMIDA VII Simposio de Teoría y Aplicaciones de Minería de Datos)},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}