Publications

@article{HABTEMARIAM25,

title = {A novel approach based on clustering and optimized ensemble deep learning for energy consumption forecasting in Ethiopia},

author = {E. T. Habtemariam and M. Martínez-Ballesteros and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S092523122500699X},

doi = {https://doi.org/10.1016/j.neucom.2025.130027},

year  = {2025},

date = {2025-03-13},

urldate = {2025-03-13},

journal = {Neurocomputing},

volume = {637},

pages = {130027},

abstract = {Predicting energy consumption accurately is crucial for optimizing energy management strategies and achieving sustainability goals. Traditional methods often struggle with the complexity of energy consumption patterns, particularly in developing regions such as Ethiopia, where unique challenges exist. This study proposes an ensemble deep learning approach that integrates multiple models to enhance prediction accuracy.

Additionally, as a previous step, a clustering process has been applied to discover different groups of customers. Our method combines deep learning architectures, including Gated Recurrent Units, Long Short-Term Memory, and Convolutional Neural Networks, within an optimized ensemble with weights computed with the Coronavirus Optimization Algorithm. This approach aims to leverage the strengths of each model

to produce robust and reliable predictions. We demonstrate that our ensemble approach yields competitive results, outperforming individual models within the ensemble. By integrating diverse models, our framework captures nuanced patterns in energy consumption data more effectively, contributing to improved prediction accuracy. Furthermore, we validate the effectiveness of our approach using three distinct datasets from Ethiopia for three different customer clusters. These datasets represent different regions and consumption profiles within the country, ensuring the robustness and generalizability of our proposed methodology.},

keywords = {clustering, deep learning, energy, forecasting},

pubstate = {published},

tppubtype = {article}

}

@article{SCITOVSKI25,

title = {A partitioning incremental algorithm using adaptive Mahalanobis fuzzy clustering and identifying the most appropriate partition},

author = {R. Scitovski and K. Sabo and D. Grahovac and F. Martínez-Álvarez and S. Ungar},

url = {https://link.springer.com/article/10.1007/s10044-024-01360-2},

doi = {https://doi.org/10.1007/s10044-024-01360-2},

year  = {2025},

date = {2025-01-02},

journal = {Pattern Analysis and Applications},

volume = {28},

pages = {3},

abstract = {This paper deals with the problem of determining the most appropriate number of clusters in a fuzzy Mahalanobis partition. 

First, a new fuzzy Mahalanobis incremental algorithm is constructed to search for an optimal fuzzy Mahalanobis 

partition with 2, 3, ... clusters. Among these partitions, selecting the one with the most appropriate number of clusters 

is based on appropriately modified existing fuzzy indexes. In addition, the Fuzzy Mahalanobis Minimal Distance index 

is defined as a natural extension of the recently proposed Mahalanobis Minimal Distance index for non-fuzzy clustering. 

The new fuzzy Mahalanobis incremental algorithm was tested on several artificial data sets and the color image segmentation 

problems from real-world applications: art images, nature photography images, and medical images. The algorithm 

includes multiple usage of the global optimization algorithm DIRECT. But unlike previously known fuzzy Mahalanobis 

indexes, the proposed Fuzzy Mahalanobis Minimal Distance index ensures accurate results even when applied to complex 

real-world applications. A possible disadvantage could be the need for longer CPU time. Furthermore, besides effective 

identification of the partition with the most appropriate number of clusters, it is shown how to use the proposed Fuzzy 

Mahalanobis Minimal Distance index to search for an acceptable partition, which proved particularly useful in the abovementioned 

real-world applications.},

keywords = {clustering},

pubstate = {published},

tppubtype = {article}

}

@article{rodriguez2025binrec,

title = {BinRec: addressing data sparsity and cold-start challenges in recommender systems with biclustering},

author = {D. Rodríguez-Baena and F. Gómez-Vela and A. Lopez-Fernandez and M. García-Torres and F. Divina},

url = {https://link.springer.com/article/10.1007/s10489-025-06725-6},

doi = {10.1007/s10489-025-06725-6},

year  = {2025},

date = {2025-01-01},

journal = {Applied Intelligence},

volume = {55},

number = {12},

pages = {830},

abstract = {Recommender Systems help users in making decision in different fields such as purchases or what movies to watch. User-Based Collaborative Filtering (UBCF) approach is one of the most commonly used techniques for developing these software tools. It is based on the idea that users who have previously shared similar tastes will almost certainly share similar tastes in the future. As a result, determining the nearest users to the one for whom recommendations are sought (active user) is critical. However, the massive growth of online commercial data has made this task especially difficult. As a result, Biclustering techniques have been used in recent years to perform a local search for the nearest users in subgroups of users with similar rating behaviour under a subgroup of items (biclusters), rather than searching the entire rating database. Nevertheless, due to the large size of these databases, the number of biclusters generated can be extremely high, making their processing very complex. In this paper we propose BinRec, a novel UBCF approach based on Biclustering. BinRec simplifies the search for neighbouring users by determining which ones are nearest to the active user based on the number of biclusters shared by the users. Experimental results show that BinRec outperforms other state-of-the-art recommender systems, with a remarkable improvement in environments with high data sparsity. The flexibility and scalability of the method position it as an efficient alternative for common collaborative filtering problems such as sparsity or cold-start.},

keywords = {clustering, pattern recognition},

pubstate = {published},

tppubtype = {article}

}

@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}

}