UNIVERSIDAD PABLO DE OLAVIDE
Publications
2024
D. Gutiérrez-Avilésand J. F. Torresand F. Martínez-Álvarezand J. Cugliari
An evolutionary triclustering approach to discover electricity consumption patterns in France Conference
SAC 39th Annual ACM Symposium on Applied Computing, 2024.
Abstract | BibTeX | Tags: clustering, energy, time series
@conference{GUTIERREZ24_SAC,
title = {An evolutionary triclustering approach to discover electricity consumption patterns in France},
author = {D. Gutiérrez-Avilés and J. F. Torres and F. Martínez-Álvarez and J. Cugliari},
year = {2024},
date = {2024-02-04},
booktitle = {SAC 39th Annual ACM Symposium on Applied Computing},
pages = {386-394},
abstract = {Electricity consumption patterns are critical in shaping energy policies
and optimizing resource allocation. In pursuing a more sustainable
and efficient energy future, uncovering hidden consumption
patterns is paramount. This paper introduces an innovative approach,
leveraging evolutionary triclustering techniques, to unveil
previously undisclosed electricity consumption patterns in France.
By harnessing the power of triclustering algorithms, this research
provides a comprehensive analysis of electricity usage across various
dimensions, shedding light on intricate relationships among
variables. Using this novel method, the study reveals concealed
patterns and offers insights that can inform decision-makers and
stakeholders in the energy sector. The findings contribute to a better
understanding of electricity consumption dynamics, aiding in
developing more targeted and effective energy management strategies.
This research represents a significant step forward in the quest
for sustainable energy solutions and underscores the potential of
evolutionary triclustering as a valuable tool in uncovering complex
consumption patterns.},
keywords = {clustering, energy, time series},
pubstate = {published},
tppubtype = {conference}
}
Electricity consumption patterns are critical in shaping energy policies
and optimizing resource allocation. In pursuing a more sustainable
and efficient energy future, uncovering hidden consumption
patterns is paramount. This paper introduces an innovative approach,
leveraging evolutionary triclustering techniques, to unveil
previously undisclosed electricity consumption patterns in France.
By harnessing the power of triclustering algorithms, this research
provides a comprehensive analysis of electricity usage across various
dimensions, shedding light on intricate relationships among
variables. Using this novel method, the study reveals concealed
patterns and offers insights that can inform decision-makers and
stakeholders in the energy sector. The findings contribute to a better
understanding of electricity consumption dynamics, aiding in
developing more targeted and effective energy management strategies.
This research represents a significant step forward in the quest
for sustainable energy solutions and underscores the potential of
evolutionary triclustering as a valuable tool in uncovering complex
consumption patterns.
and optimizing resource allocation. In pursuing a more sustainable
and efficient energy future, uncovering hidden consumption
patterns is paramount. This paper introduces an innovative approach,
leveraging evolutionary triclustering techniques, to unveil
previously undisclosed electricity consumption patterns in France.
By harnessing the power of triclustering algorithms, this research
provides a comprehensive analysis of electricity usage across various
dimensions, shedding light on intricate relationships among
variables. Using this novel method, the study reveals concealed
patterns and offers insights that can inform decision-makers and
stakeholders in the energy sector. The findings contribute to a better
understanding of electricity consumption dynamics, aiding in
developing more targeted and effective energy management strategies.
This research represents a significant step forward in the quest
for sustainable energy solutions and underscores the potential of
evolutionary triclustering as a valuable tool in uncovering complex
consumption patterns.
2023
D. Hadjoutand A. Sebaaand J. F. Torresand F. Mártinez-Álvarez
Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market Journal Article
In: Expert Systems with Applications, vol. 227, pp. 120123, 2023.
Abstract | Links | BibTeX | Tags: clustering, deep learning, energy, time series
@article{HADJOUT23,
title = {Electricity consumption forecasting with outliers handling based on clustering and deep learning with application to the Algerian market},
author = {D. Hadjout and A. Sebaa and J. F. Torres and F. Mártinez-Álvarez},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0957417423006255},
doi = {https://doi.org/10.1016/j.eswa.2023.120123},
year = {2023},
date = {2023-10-01},
journal = {Expert Systems with Applications},
volume = {227},
pages = {120123},
abstract = {The reduction of electricity loss and the effective management of electricity demand are vital operations for production and distribution electricity enterprises. To achieve these goals, accurate forecasts of aggregate and individual electricity consumers are necessary. A novel multistep forecasting method is developed to forecast medium-term electricity consumption of the Algerian economic sector. The proposed method goes through the following three steps: cleaning steps, clustering steps and forecasting step of each cluster. The aim of the first step is to detect and then replace outliers. To complete the first phase, Robust Exponential and Holt-Winters Smoothing algorithms are adapted. Then, to carry out accurate forecasting at a lowest level, K-Shape and K-Means clustering methods are utilized to extract similarities and identify customer consumption patterns as a second step. The third step entails developing a deep learning model based on Gated Recurrent Units to forecast the electricity consumption in each cluster. To validate the proposed method, we compared our results to the most known methods in literature like Autoregressive Integrated Moving Average, Seasonal Grey Model, LSTM networks, Temporal Convolutional Networks and two ensemble models. The results of several experiments conducted with 2000 electricity consumers during 14 years from an Algeria province (Bejaia) demonstrate that the proposed method provides remarkable prediction performances. Thus, prediction performances of the K-Shape-based clustering method reach much higher prediction accuracy. According to the MAPE metric, the results of the best predictions are equal to 2.04%. It is also notable that 87% of the clients have a considerably low prediction error.},
keywords = {clustering, deep learning, energy, time series},
pubstate = {published},
tppubtype = {article}
}
The reduction of electricity loss and the effective management of electricity demand are vital operations for production and distribution electricity enterprises. To achieve these goals, accurate forecasts of aggregate and individual electricity consumers are necessary. A novel multistep forecasting method is developed to forecast medium-term electricity consumption of the Algerian economic sector. The proposed method goes through the following three steps: cleaning steps, clustering steps and forecasting step of each cluster. The aim of the first step is to detect and then replace outliers. To complete the first phase, Robust Exponential and Holt-Winters Smoothing algorithms are adapted. Then, to carry out accurate forecasting at a lowest level, K-Shape and K-Means clustering methods are utilized to extract similarities and identify customer consumption patterns as a second step. The third step entails developing a deep learning model based on Gated Recurrent Units to forecast the electricity consumption in each cluster. To validate the proposed method, we compared our results to the most known methods in literature like Autoregressive Integrated Moving Average, Seasonal Grey Model, LSTM networks, Temporal Convolutional Networks and two ensemble models. The results of several experiments conducted with 2000 electricity consumers during 14 years from an Algeria province (Bejaia) demonstrate that the proposed method provides remarkable prediction performances. Thus, prediction performances of the K-Shape-based clustering method reach much higher prediction accuracy. According to the MAPE metric, the results of the best predictions are equal to 2.04%. It is also notable that 87% of the clients have a considerably low prediction error.
P. García-Bringasand H. Pérez-Garcíaand F. J. Martínez de Pisónand F. Martínez-Álvarezand A. Troncosoand Á. Herreroand J. L. Calvo-Rolleand H. Quintiánand E. Corchado
Springer, vol. 749, 2023, ISBN: 978-3-031-42529-5.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{SOCO2023a,
title = {Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) Salamanca, Spain, September 5-7, 2023, volume 1},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-42529-5},
doi = {https://doi.org/10.1007/978-3-031-42529-5},
isbn = {978-3-031-42529-5},
year = {2023},
date = {2023-09-05},
volume = {749},
publisher = {Springer},
series = {Lecture Notes in Networks and Systems},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
P. García-Bringasand H. Pérez-Garcíaand F. J. Martínez de Pisónand F. Martínez-Álvarezand A. Troncosoand Á. Herreroand J. L. Calvo-Rolleand H. Quintiánand E. Corchado
Springer, vol. 750, 2023, ISBN: 978-3-031-42536-3.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{SOCO2023b,
title = {Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) Salamanca, Spain, September 5-7, 2023, volume 2},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-42536-3},
doi = {10.1007/978-3-030-20055-8},
isbn = {978-3-031-42536-3},
year = {2023},
date = {2023-09-05},
volume = {750},
publisher = {Springer},
series = {Lecture Notes in Networks and Systems},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
P. García-Bringasand H. Pérez-Garcíaand F. J. Martínez de Pisónand F. Martínez-Álvarezand A. Troncosoand Á. Herreroand J. L. Calvo-Rolleand H. Quintiánand E. Corchado
Springer, vol. 14001, 2023, ISBN: 978-3-031-40725-3.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{HAIS2023,
title = {Proceedings of the 18th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2023) Salamanca, Spain, September 5-7, 2023},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-40725-3},
doi = {https://doi.org/10.1007/978-3-031-40725-3},
isbn = {978-3-031-40725-3},
year = {2023},
date = {2023-09-05},
volume = {14001},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
P. García-Bringasand H. Pérez-Garcíaand F. J. Martínez de Pisónand F. Martínez-Álvarezand A. Troncosoand Á. Herreroand J. L. Calvo-Rolleand H. Quintiánand E. Corchado
Springer, vol. 748, 2023, ISBN: 978-3-031-42519-6.
Links | BibTeX | Tags: big data, clustering
@proceedings{CISIS-ICEUTE2023,
title = {Proceedings of the International Joint Conference 16th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2023) 14th International Conference on EUropean Transnational Education (ICEUTE 2023). Salamanca, Spain, September 5-7, 2023},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
editor = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and F. Martínez-Álvarez and A. Troncoso and Á. Herrero and J. L. Calvo-Rolle and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-42519-6},
doi = {https://doi.org/10.1007/978-3-031-42519-6},
isbn = {978-3-031-42519-6},
year = {2023},
date = {2023-09-05},
volume = {748},
publisher = {Springer},
series = {Lecture Notes in Networks and Systems},
keywords = {big data, clustering},
pubstate = {published},
tppubtype = {proceedings}
}
2022
P. García-Bringasand H. Pérez-Garcíaand F. J. Martínez de Pisónand J. R. Villar-Flechaand A. Troncosoand E. A. de la Caland Á. Herreroand F. Martínez-Álvarezand G. Psailaand H. Quintiánand E. Corchado
Springer, vol. 13469, 2022, ISBN: 978-3-031-15470-6.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{HAIS2022,
title = {Proceedings of the 17th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2022) Salamanca, Spain, September 5-7, 2022},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-15471-3},
doi = {https://doi.org/10.1007/978-3-031-15471-3},
isbn = {978-3-031-15470-6},
year = {2022},
date = {2022-09-05},
volume = {13469},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}
P. García-Bringasand H. Pérez-Garcíaand F. J. Martínez de Pisónand J. R. Villar-Flechaand A. Troncosoand E. A. de la Caland Á. Herreroand F. Martínez-Álvarezand G. Psailaand H. Quintiánand E. Corchado
Springer, vol. 531, 2022, ISBN: 978-3-031-18050-7.
Links | BibTeX | Tags: big data, clustering, deep learning, IoT
@proceedings{SOCO2022,
title = {Proceedings of the 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) Salamanca, Spain, September 5-7, 2022},
author = {P. García-Bringas and H. Pérez-García and F. J. Martínez de Pisón and J. R. Villar-Flecha and A. Troncoso and E. A. de la Cal and Á. Herrero and F. Martínez-Álvarez and G. Psaila and H. Quintián and E. Corchado},
url = {https://link.springer.com/book/10.1007/978-3-031-18050-7},
doi = {https://doi.org/10.1007/978-3-031-18050-7},
isbn = {978-3-031-18050-7},
year = {2022},
date = {2022-09-05},
volume = {531},
publisher = {Springer},
series = {Lecture Notes in Networks and Systems},
keywords = {big data, clustering, deep learning, IoT},
pubstate = {published},
tppubtype = {proceedings}
}