@article{GARCIA-SOTO24,

title = {Water consumption time series forecasting in urban centers using deep neural networks},

author = {C. G. García-Soto and J. F. Torres and M. A. Zamora-Izquierdo and J. Palma and A. Troncoso},

url = {https://link.springer.com/article/10.1007/s13201-023-02072-4},

doi = {https://doi.org/10.1007/s13201-023-02072-4},

year  = {2024},

date = {2024-01-12},

journal = {Applied Water Science},

volume = {14},

pages = {1-14},

abstract = {The time series analysis and prediction techniques are highly valued in many application felds, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for diferent time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifcally designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AIMS_Math2024,

title = {Special issue on Advances in Time Series Forecasting},

author = {J. F. Torres and M. Martinez-Ballesteros and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.aimspress.com/article/doi/10.3934/math.20241174},

doi = {10.3934/math.20241174},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {AIMS Mathematics},

volume = {9},

issue = {9},

pages = {24163-24165},

keywords = {},

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 = {},

pubstate = {published},

tppubtype = {article}

}

@article{HADJOUT22,

title = {Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market},

author = {D. Hadjout and J. F. Torres and A. Troncoso and A. Sebaa and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0360544221033090},

doi = {https://doi.org/10.1016/j.energy.2021.123060},

year  = {2022},

date = {2022-03-15},

journal = {Energy},

volume = {243},

pages = {123060},

abstract = {The economic sector is one of the most important pillars of countries. Economic activities of industry are intimately linked with the ability to meet their needs for electricity. Therefore, electricity forecasting is a very important task. It allows for better planning and management of energy resources. Several methods have been proposed to forecast energy consumption. In this work, to predict monthly electricity consumption for the economic sector, we develop a novel approach based on ensemble learning. Our approach combines three models that proved successful in the field, namely: Long Short Term Memory and Gated Recurrent Unit neural networks, and Temporal Convolutional Networks. The experiments have been conducted with almost 2000 clients and 14 years of monthly electricity consumption from Bejaia, Algeria. The results show that the proposed ensemble models achieve better performance than both the company's requirements and the prediction of the traditional individual models. Finally, statistical tests have been carried out to prove that significance of the ensemble models developed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TORRES22b,

title = {A deep LSTM network for the Spanish electricity consumption forecasting},

author = {J. F. Torres and F. Martínez-Álvarez and A. Troncoso},

url = {https://link.springer.com/article/10.1007/s00521-021-06773-2},

doi = {https://doi.org/10.1007/s00521-021-06773-2},

year  = {2022},

date = {2022-02-05},

journal = {Neural Computing and Applications},

volume = {34},

pages = {10533-10545},

abstract = {Nowadays, electricity is a basic commodity necessary for the well-being of any modern society. Due to the growth in electricity consumption in recent years, mainly in large cities, electricity forecasting is key to the management of an efficient, sustainable and safe smart grid for the consumer. In this work, a deep neural network is proposed to address the electricity consumption forecasting in the short-term, namely, a long short-term memory (LSTM) network due to its ability to deal with sequential data such as time-series data. First, the optimal values for certain hyper-parameters have been obtained by a random search and a metaheuristic, called coronavirus optimization algorithm (CVOA), based on the propagation of the SARS-Cov-2 virus. Then, the optimal LSTM has been applied to predict the electricity demand with 4-h forecast horizon. Results using Spanish electricity data during nine years and half measured with 10-min frequency are presented and discussed. Finally, the performance of the proposed LSTM using random search and the LSTM using CVOA is compared, on the one hand, with that of recently published deep neural networks (such as a deep feed-forward neural network optimized with a grid search) and temporal fusion transformers optimized with a sampling algorithm, and, on the other hand, with traditional machine learning techniques, such as a linear regression, decision trees and tree-based ensemble techniques (gradient-boosted trees and random forest), achieving the smallest prediction error below 1.5%.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BUI22b,

title = {Deformation forecasting of a hydropower dam by hybridizing a Long Short-Term Memory deep learning network with the Coronavirus Optimization Algorithm},

author = {K.-T. T. Bui and J. F. Torres and D. Gutiérrez-Avilés and V. H. Nhu and F. Martínez-Álvarez and D. T. Bui},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/mice.12810},

doi = {https://doi.org/10.1111/mice.12810},

year  = {2021},

date = {2021-11-24},

journal = {Computer-Aided Civil and Infrastructure Engineering},

volume = {37},

pages = {1368-1386},

abstract = {The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people's safety in many countries; therefore, modeling and forecasting the hydropower dam's deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the deformations of the hydropower dam. The second-largest hydropower dam of Vietnam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to optimize the three parameters of the LSTM, the number of hidden layers, the learning rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art benchmarks, sequential minimal optimization for support vector regression, Gaussian process, M5' model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural network. The result shows that the proposed CVOA-LSTM model has high forecasting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam's deformations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TORRES21,

title = {Deep Learning for Time Series Forecasting: A Survey},

author = {J. F. Torres and D. Hadjout and A. Sebaa and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.liebertpub.com/doi/10.1089/big.2020.0159},

doi = {10.1089/big.2020.0159},

year  = {2021},

date = {2021-02-05},

journal = {Big Data},

volume = {9},

number = {1},

pages = {3-21},

abstract = {Deep learning, one of the most remarkable techniques of machine learning, has been a major success in many fields, including image processing, speech recognition, and text understanding. It is powerful engines capable of learning arbitrary mapping functions, not require a scaled or stationary time series as input, support multivariate inputs, and support multi-step outputs. All of these features together make deep learning useful tools when dealing with more complex time series prediction problems involving large amounts of data, and multiple variables with complex relationships. This paper provides an overview of the most common Deep Learning types for time series forecasting, Explain the relationships between deep learning models and classical approaches to time series forecasting. A brief background of the particular challenges presents in time-series data and the most common deep learning techniques that are often used for time series forecasting is provided. Previous studies that applied deep learning to time series are reviewed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}