Publications

@article{Energies2016,

title = {A new methodology based on imbalanced classification for predicting outliers in electricity demand time series},

author = {F. J. Duque-Pintor and M. J. Fernández-Gómez and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.mdpi.com/1996-1073/9/9/752},

doi = {10.3390/en9090752},

year  = {2016},

date = {2016-01-01},

journal = {Energies},

volume = {9},

number = {9},

pages = {752},

abstract = {The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new methodology to a priori predict the occurrence of such data is proposed here. Thus, the main goal of this work is to predict the occurrence of outliers in time series, by using, for the first time, imbalanced classification techniques. In this sense, the problem of forecasting outlying data has been transformed into a binary classification problem, in which the positive class represents the occurrence of outliers. Given that the number of outliers is much lower than the number of common values, the resultant classification problem is imbalanced. To create training and test sets, robust statistical methods have been used to detect outliers in both sets. Once the outliers have been detected, the instances of the dataset are labeled accordingly. Namely, if any of the samples composing the next instance are detected as an outlier, the label is set to one. As a study case, the methodology has been tested on electricity demand time series in the Spanish electricity market, in which most of the outliers were properly forecast.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{RENE2015,

title = {Local models regression trees for very short-term wind speed predictions},

author = {A. Troncoso and S. Salcedo-Sanz and C. Casanova-Mateo and J. C. Riquelme and L. Prieto},

url = {https://www.sciencedirect.com/science/article/pii/S0960148115002530},

doi = {10.1016/j.renene.2015.03.071},

year  = {2015},

date = {2015-01-01},

journal = {Renewable Energy},

volume = {81},

pages = {589-598},

abstract = {This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term wind speed prediction from measuring data in wind farms. RT is a solidly established methodology that, contrary to other soft-computing approaches, has been under-explored in problems of wind speed prediction in wind farms. In this paper we comparatively evaluate eight different types of RTs algorithms, and we show that they are able obtain excellent results in real problems of very short-term wind speed prediction, improving existing classical and soft-computing approaches such as multi-linear regression approaches, different types of neural networks and support vector regression algorithms in this problem. We also show that RTs have a very small computation time, that allows the retraining of the algorithms whenever new wind speed data are collected from the measuring towers.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2015,

title = {A Survey on Data Mining Techniques Applied To Electricity-Related Time Series Forecasting},

author = {F. Martínez-Álvarez and A. Troncoso and G. Asencio-Cortés and J. C. Riquelme},

url = {https://www.mdpi.com/1996-1073/8/11/12361},

doi = {10.3390/en81112361},

year  = {2015},

date = {2015-01-01},

journal = {Energies},

volume = {8},

number = {11},

pages = {13162-13193},

abstract = {Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{TKDE2011,

title = {Energy Time Series Forecasting Based on Pattern Sequence Similarity},

author = {F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme and J. S. Aguilar-Ruiz},

url = {https://ieeexplore.ieee.org/document/5620917},

doi = {10.1109/TKDE.2010.227},

year  = {2011},

date = {2011-01-01},

journal = {IEEE Transactions on Knowledge and Data Engineering},

volume = {23},

number = {8},

pages = {1230-1243},

abstract = {This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction process. Results from several energy time series are reported and the performance of the proposed method is compared to that of recently published techniques showing a remarkable improvement in the prediction.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{PRL2011,

title = {Discovery of Motifs for Forecast Outlier Occurrence in Time Series},

author = {F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme and J. S. Aguilar-Ruiz},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0167865511001371},

doi = {10.1016/j.patrec.2011.05.002},

year  = {2011},

date = {2011-01-01},

journal = {Pattern Recognition Letters},

number = {32},

pages = {1652–1665},

abstract = {The forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the identification of future outlier occurrence is an essential task in time series analysis to reduce the average forecasting error. The main goal of this work is to predict the occurrence of outliers in time series, based on the discovery of motifs. In this sense, motifs will be those pattern sequences preceding certain data marked as anomalous by the proposed metaheuristic in a training set. Once the motifs are discovered, if data to be predicted are preceded by any of them, such data are identified as outliers, and treated separately from the rest of regular data. The forecasting of outlier occurrence has been added as an additional step in an existing time series forecasting algorithm (PSF), which was based on pattern sequence similarities. Robust statistical methods have been used to evaluate the accuracy of the proposed approach regarding the forecasting of both occurrence of outliers and their corresponding values. Finally, the methodology has been tested on six electricity-related time series, in which most of the outliers were properly found and forecasted.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{EJOR2008,

title = {Evolutionary Techniques Applied to the Optimal Short-Term Scheduling of the Electrical Energy Production},

author = {A. Troncoso and J. C. Riquelme and J. S. Aguilar-Ruiz and J. M. Riquelme-Santos},

url = {https://www.sciencedirect.com/science/article/abs/pii/S037722170600631X},

doi = {10.1016/j.ejor.2006.06.044},

year  = {2008},

date = {2008-01-01},

journal = {European Journal of Operational Research},

volume = {185},

pages = {1114-1127},

abstract = {This paper presents an evolutionary technique applied to the optimal short-term scheduling (24 h) of the electric energy production. The equations that define the problem lead to a non-convex non-linear programming problem with a high number of continuous and discrete variables. Consequently, the resolution of the problem based on combinatorial methods is rather hard. The required heuristics, introduced to assure the feasibility of the constraints, are analyzed, along with a brief description of the proposed genetic algorithm (GA). The GA is used to compute the optimal on/off status of thermal units and the fitness function is obtained by solving a quadratic programming problem by means of a standard non-linear Interior Point (IP) method. The results from real-world cases based on the Spanish power system are reported, which show the good performance of the proposed algorithm, taking into account the complexity and dimensionality of the problem. Finally, an IP algorithm is adapted to deal with discrete variables that appear in this problem and the obtained results are compared with that of the proposed GA.},

keywords = {energy},

pubstate = {published},

tppubtype = {article}

}

@article{IEEE2007,

title = {Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques},

author = {A. Troncoso and J. M. Riquelme-Santos and A. Gómez-Expósito and J. L. Martínez-Ramos and J. C. Riquelme},

url = {https://ieeexplore.ieee.org/document/4282040},

doi = {10.1109/TPWRS.2007.901670},

year  = {2007},

date = {2007-01-01},

journal = {IEEE Transactions on Power Systems},

volume = {22},

number = {3},

pages = {1294-1301},

abstract = {This paper presents a simple technique to forecast next-day electricity market prices based on the weighted nearest neighbors methodology. First, it is explained how the relevant parameters defining the adopted model are obtained. Such parameters have to do with the window length of the time series and with the number of neighbors chosen for the prediction. Then, results corresponding to the Spanish electricity market during 2002 are presented and discussed. Finally, the performance of the proposed method is compared with that of recently published techniques.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{AICOM2005,

title = {Advances in Optimization and Prediction Techniques: Real-World Applications},

author = {A. Troncoso},

url = {https://content.iospress.com/articles/ai-communications/aic372},

year  = {2006},

date = {2006-01-01},

journal = {Artificial Intelligence Communications},

volume = {19},

number = {3},

pages = {295-297},

abstract = {This paper describes a time-series prediction method based on the k-Weighted Nearest Neighbours (k-WNN) algorithm and a simple technique to deal with nonconvex, nonlinear optimization problems by solving a sequence of Interior Point (IP) subproblems. The proposed prediction methodology is applied to obtain the 24-hour forecasts of two real time series: the demand and the energy prices in the competitive Spanish Electricity Market. The proposed optimization method is applied to the optimal scheduling of the electric energy production in the short-term.},

keywords = {energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{IEEE2003,

title = {Finding Improved Local Minima of Power System Optimization Problems by Interior-Point Methods},

author = {J. M. Riquelme-Santos and A. Troncoso and A. Gómez-Expósito and J. L. Martínez-Ramos},

url = {https://ieeexplore.ieee.org/document/1178802},

doi = {10.1109/TPWRS.2002.807097},

year  = {2003},

date = {2003-01-01},

journal = {IEEE Transactions on Power Systems},

volume = {18},

number = {3},

pages = {238-244},

abstract = {This paper presents a simple heuristic technique to deal with multiple local minima in nonconvex, nonlinear power system optimization problems by solving a sequence of interior-point subproblems. Both the real-valued and the mixed-integer cases are separately discussed. The method is then applied to the unit commitment problem and its performance on realistic cases is compared with that of a genetic algorithm (GA).},

keywords = {energy},

pubstate = {published},

tppubtype = {article}

}