Project

ALGORITMOS DE CLASIFICACION ORDINAL Y PREDICCION EN ENERGIAS RENOVABLES (ORDINAL CLASSIFICATION AND PREDICTION ALGORITHMS IN RENAWABLE ENERGY, ORCA-RE) TIN2014-54583-C2-1-R Financial Entity: Ministerio de Economía y Competitividad.MINECO

Goal: Given that fossil energy resources will not satisfy the energy demand of the world population within a relatively short period of time, a very important research trend is now investigating in alternative sources of efficient, reliable and clean energy, to boost the performance of current infrastructures. Predicting the amount of energy produced is essential for assuring an effective inclusion of these energies in the electrical network. This kind of energies are associated to physical phenomena with an important unknown random component, producing high variability. In this way, their prediction is not affordable, in general, using classical predictive methodologies. Because of this, the coordinated project ORCA-RE is aimed to explore, develop and extend various machine learning methodologies to tackle the problem of production estimation of Wind Energy, Solar Energy and Wave Energy. Different paradigms, such as ordinal classification or time series segmentation, will be analysed, which have a great interest for the central problem of this project and which have been scarcely studied in comparison to nominal classification, both in Spain and in the rest of the world. Both research groups have previous, coordinated and contrasted experience in this field. The predictions based on ordinal classification will be compared to the use of segmentation, to evaluate the influence of the temporal component. In this way, we are pursuing the following objectives related with the challenge of alternative energy sources:
1) To use Computational Intelligence techniques to develop new ordinal classification models and imbalanced ordinal classification models, and to analyse new classifier evaluation metrics. Use of mono and multi-objective hybrid methodologies (this last paradigm needed because some of the classifier evaluation metrics are opposite).
2) To develop time series segmentation algorithms applied to renewable energies based on statistical and bio-inspired methods. Medium-term prediction using the result of these segmentation algorithms.
3) To develop new bio-inspired models for the evolution of classifiers and regressors using grouping genetic algorithms and new single population co-evolution models using the Coral Reefs Optimization paradigm.
4) To apply these models to different renewable energy problems, mainly prediction and resource estimation in Wind Energy, Solar Energy and Wave Energy. Application to other real problems of time series segmentation and prediction.
5) To develop a software package to be used in the framework NNEP incorporating all the new models developed in the project, and a package for the WEKA framework incorporating some of the main ordinal classification methods, which would allow the spreading of this paradigm in the scientific community.

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Project log

Juan Carlos Fernández
added a research item
This work proposes a hybrid methodology for the detection and prediction of Extreme Significant Wave Height (ESWH) periods in oceans. In a first step, wave height time series is approximated by a labeled sequence of segments, which is obtained using a genetic algorithm in combination with a likelihood-based segmentation (GA+LS). Then, an artificial neural network classifier with hybrid basis functions is trained with a multiobjetive evolutionary algorithm (MOEA) in order to predict the occurrence of future ESWH segments based on past values. The methodology is applied to a buoy in the Gulf of Alaska and another one in Puerto Rico. The results show that the GA+LS is able to segment and group the ESWH values, and the neural network models, obtained by the MOEA, make good predictions maintaining a balance between global accuracy and minimum sensitivity for the detection of ESWH events. Moreover, hybrid neural networks are shown to lead to better results than pure models.
Juan Carlos Fernández
added a research item
In this paper, the performance of different ordinal and nominal multi-class classifiers is evaluated, in a problem of wave energy range prediction using meteorological variables from numerical models. This prediction could be used in problems of wave energy conversion in renewable and sustainable systems for energy supply. Specifically, the work is focused on ordinal classifiers, that have provided excellent performance in previous applications. The proposed techniques are novel with respect to alternative classification and regression techniques used up to date, the former not considering the order relation between classes in a multi-class problem and the latter needing, in general, more complex models. Another important novelty of the paper is to consider meteorological variables from numerical models as inputs of the classifiers, which has not been done before, to our knowledge, in this context. For this, a data matching is carried out between meteorological data, obtained from NCEP/NCAR Reanalysis Project in four points around the two buoys subjected to study (a buoy in the Gulf of Alaska and another one in the Southeast of United States), and the wave height or wave period collected by sensors in each buoy. Using this matching, the problem is tackled as an ordinal multi-class classification problem and the objective is to predict the range of height of the wave produced in each buoy and the range of energy flux generated. The classifiers to be compared and the model proposed are fully evaluated in both buoys. The results obtained are promising, showing an acceptable reconstruction by ordinal methods with respect to nominal ones in terms of wave height and energy flux.
Juan Carlos Fernández
added a project goal
Given that fossil energy resources will not satisfy the energy demand of the world population within a relatively short period of time, a very important research trend is now investigating in alternative sources of efficient, reliable and clean energy, to boost the performance of current infrastructures. Predicting the amount of energy produced is essential for assuring an effective inclusion of these energies in the electrical network. This kind of energies are associated to physical phenomena with an important unknown random component, producing high variability. In this way, their prediction is not affordable, in general, using classical predictive methodologies. Because of this, the coordinated project ORCA-RE is aimed to explore, develop and extend various machine learning methodologies to tackle the problem of production estimation of Wind Energy, Solar Energy and Wave Energy. Different paradigms, such as ordinal classification or time series segmentation, will be analysed, which have a great interest for the central problem of this project and which have been scarcely studied in comparison to nominal classification, both in Spain and in the rest of the world. Both research groups have previous, coordinated and contrasted experience in this field. The predictions based on ordinal classification will be compared to the use of segmentation, to evaluate the influence of the temporal component. In this way, we are pursuing the following objectives related with the challenge of alternative energy sources:
1) To use Computational Intelligence techniques to develop new ordinal classification models and imbalanced ordinal classification models, and to analyse new classifier evaluation metrics. Use of mono and multi-objective hybrid methodologies (this last paradigm needed because some of the classifier evaluation metrics are opposite).
2) To develop time series segmentation algorithms applied to renewable energies based on statistical and bio-inspired methods. Medium-term prediction using the result of these segmentation algorithms.
3) To develop new bio-inspired models for the evolution of classifiers and regressors using grouping genetic algorithms and new single population co-evolution models using the Coral Reefs Optimization paradigm.
4) To apply these models to different renewable energy problems, mainly prediction and resource estimation in Wind Energy, Solar Energy and Wave Energy. Application to other real problems of time series segmentation and prediction.
5) To develop a software package to be used in the framework NNEP incorporating all the new models developed in the project, and a package for the WEKA framework incorporating some of the main ordinal classification methods, which would allow the spreading of this paradigm in the scientific community.