Daniel Pérez López

@inproceedings{Garcia2023conditioned,

title = {Conditioned Fully Convolutional Denoising Autoencoder for Energy Disaggregation},

author = {Diego García and Daniel Pérez and Panagiotis Papapetrou and Ignacio Díaz and Abel A Cuadrado and José María Enguita and Ana González and Manuel Domínguez},

doi = {10.1007/978-3-031-34171-7_34},

isbn = {978-3-031-34204-2},

year  = {2023},

date = {2023-06-07},

urldate = {2023-06-07},

booktitle = {IFIP International Conference on Artificial Intelligence Applications and Innovations},

pages = {421–433},

publisher = {Springer Nature Switzerland},

address = {Cham},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-34204-2_21,

title = {Hierarchical Prediction in Incomplete Submetering Systems Using a CNN},

author = {Serafín Alonso and Antonio Morán and Daniel Pérez and Miguel A Prada and Juan J Fuertes and Manuel Domínguez},

doi = {10.1007/978-3-031-34204-2_21},

isbn = {978-3-031-34204-2},

year  = {2023},

date = {2023-06-07},

urldate = {2023-06-07},

booktitle = {Engineering Applications of Neural Networks},

pages = {239--250},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Energy market liberalization brings new opportunities, since large consumers have direct access to energy trading to buy energy for the next day. However, that requires a good estimation of the expected amount of energy and its hourly distribution in advance. On the other hand, smart energy meters are being installed in many facilities with the aim of achieving holistic submetering systems. These systems consist of a set of meters structured in several levels, so that there are hierarchical relations among upstream and downstream meters. This information could be exploited for achieving accurate one-day-ahead energy predictions. However, submetering systems might be incomplete due to unavailable meters or lost energy. In this paper, we propose a hierarchical prediction method for incomplete submetering systems that is based on 2D convolutional neural network (2D CNN) and is able to perform day ahead prediction of power consumption. This method exploits the hierarchical relations among meters and considers periodicity in order to forecast the power consumption for the next day. The proposed hierarchical method has proved to be more accurate and fast to forecast power consumption in incomplete submetering systems than using an individual predictions.},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{alonso2022Reconstructing,

title = {Reconstructing Electricity Profiles in Submetering Systems Using a GRU-AE Network},

author = {Serafín Alonso and Antonio Morán and Daniel Pérez and Miguel A Prada and Juan J Fuertes and Manuel Domínguez},

doi = {10.1007/978-3-031-08223-8_21},

issn = {1865-0937},

year  = {2022},

date = {2022-06-10},

urldate = {2022-01-01},

booktitle = {Communications in Computer and Information Science},

volume = {1600 CCIS},

pages = {247-259},

publisher = {Springer Science and Business Media Deutschland GmbH},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{alonso2020probabilistic,

title = {Probabilistic Estimation of Evaporated Water in Cooling Towers Using a Generative Adversarial Network},

author = { Serafín Alonso and Antonio Morán and Daniel Pérez and Miguel A Prada and Juan J Fuertes and Manuel Domínguez},

doi = {10.1007/978-3-030-48791-1_11},

year  = {2020},

date = {2020-01-01},

booktitle = {International Conference on Engineering Applications of Neural Networks},

pages = {155--166},

organization = {Springer, Cham},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Alonso2019,

title = {Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers},

author = {Serafín Alonso and Antonio Morán and Daniel Pérez and Perfecto Reguera and Ignacio Díaz and Manuel Domínguez},

doi = {10.1007/978-3-030-20257-6_26},

isbn = {978-3-030-20257-6},

year  = {2019},

date = {2019-05-15},

booktitle = {Communications in Computer and Information Science, International Conference on Engineering Applications of Neural Networks},

pages = {307-319},

publisher = {Springer},

organization = {EANN2019},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{García2017,

title = {Latent variable analysis in hospital electric power demand using non-negative matrix factorization },

author = {Diego García and Ignacio Díaz and Daniel Pérez and Abel A Cuadrado and Manuel Domínguez },

year  = {2017},

date = {2017-08-12},

booktitle = {Proceedings of ESANN 2017, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},

pages = {507-512},

edition = {25th},

organization = {European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{perez2014power,

title = {Power-Consumption Analysis through Web-Based Visual Data Exploration},

author = {Daniel Pérez and Ignacio Díaz and Abel A Cuadrado and Francisco J García and Alberto B Díez and Manuel Domínguez},

doi = {10.3182/20140824-6-ZA-1003.01267},

year  = {2014},

date = {2014-01-01},

booktitle = {World Congress},

volume = {19},

number = {1},

pages = {11257--11262},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{perez11,

title = {Visualization of Power Systems Consumptions by Manifold Learning.},

author = {Daniel Pérez and Ignacio Díaz and Francisco J García and Pablo Barrientos},

year  = {2011},

date = {2011-09-01},

booktitle = {Physcon 2011, 5th International Scientific Conference on Physics and Control},

keywords = {MACHINE LEARNING, SUPERVISIÓN ENERGÉTICA EN EDIFICIOS},

pubstate = {published},

tppubtype = {inproceedings}

}