2019
Salcedo-Sanz, Sancho; García-Herrera, R.; Camacho-Gómez, C.; Alexandre, E.; Carro-Calvo, L.; Jaume-Santero, F.
Near-optimal selection of representative measuring points for robust temperature field reconstruction with the CRO-SL and analogue methods Journal Article
In: Global and Planetary Change, vol. 178, pp. 15-34, 2019, ISSN: 0921-8181.
Abstract | Links | BibTeX | Tags: Analogue method, Coral Reefs Optimization, Most representative points, Temperature fields reconstruction
@article{SALCEDOSANZ201915,
title = {Near-optimal selection of representative measuring points for robust temperature field reconstruction with the CRO-SL and analogue methods},
author = {Sancho Salcedo-Sanz and R. García-Herrera and C. Camacho-Gómez and E. Alexandre and L. Carro-Calvo and F. Jaume-Santero},
url = {https://www.sciencedirect.com/science/article/pii/S0921818118306039},
doi = {https://doi.org/10.1016/j.gloplacha.2019.04.013},
issn = {0921-8181},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Global and Planetary Change},
volume = {178},
pages = {15-34},
abstract = {In this paper we tackle a problem of representative measuring points selection for temperature field reconstruction. This problem is a version of the more general Representative Selection (RS) problem, well-known in computer and data science. In this particular case, the objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a reconstruction error is minimized when reconstructing the monthly average temperature field. We use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer (CRO-SL), which is an evolutionary-type method able to combine several different search procedures within a single population. The CRO-SL is combined with the Analogue Method (AM) to identify the most representative points. This approach exhibits strong performance from experiments with gridded and un-gridded temperature field datasets (European Climate Assessment & Dataset (ECA) and ERA-Interim reanalysis (ERA)). Different aspects such as the error assessment and the comparison with alternative approaches, are discussed in the experimental analysis of this article. We show that the algorithm performs better than a greedy approach, i.e. the best solution for N points is different from the N best individual predictors. The solutions obtained with the proposed methodology are climatologically consistent and include points from Scandinavia, Central and Southern Europe, the Black Sea and Central and South Western Asia as the more representative in the case of the ECA dataset; similar areas are selected for ERA. We have found out that once the number of stations/points goes over a threshold, the improvement in the model is obtained by increasing the density of data in the given zones, instead of adding data from different zones to the algorithm. The method proposed may have direct application in Palaeoclimalogy, where there are a large amount of distributed proxies with scarce information, so the proposed approach could be useful to select the most important ones to reconstruct a desired field.},
keywords = {Analogue method, Coral Reefs Optimization, Most representative points, Temperature fields reconstruction},
pubstate = {published},
tppubtype = {article}
}
In this paper we tackle a problem of representative measuring points selection for temperature field reconstruction. This problem is a version of the more general Representative Selection (RS) problem, well-known in computer and data science. In this particular case, the objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a reconstruction error is minimized when reconstructing the monthly average temperature field. We use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer (CRO-SL), which is an evolutionary-type method able to combine several different search procedures within a single population. The CRO-SL is combined with the Analogue Method (AM) to identify the most representative points. This approach exhibits strong performance from experiments with gridded and un-gridded temperature field datasets (European Climate Assessment & Dataset (ECA) and ERA-Interim reanalysis (ERA)). Different aspects such as the error assessment and the comparison with alternative approaches, are discussed in the experimental analysis of this article. We show that the algorithm performs better than a greedy approach, i.e. the best solution for N points is different from the N best individual predictors. The solutions obtained with the proposed methodology are climatologically consistent and include points from Scandinavia, Central and Southern Europe, the Black Sea and Central and South Western Asia as the more representative in the case of the ECA dataset; similar areas are selected for ERA. We have found out that once the number of stations/points goes over a threshold, the improvement in the model is obtained by increasing the density of data in the given zones, instead of adding data from different zones to the algorithm. The method proposed may have direct application in Palaeoclimalogy, where there are a large amount of distributed proxies with scarce information, so the proposed approach could be useful to select the most important ones to reconstruct a desired field.
2018
Salcedo-Sanz, Sancho; García-Herrera, R.; Camacho-Gómez, C.; Aybar-Ruíz, A.; Alexandre, E.
Wind power field reconstruction from a reduced set of representative measuring points Journal Article
In: Applied Energy, vol. 228, pp. 1111-1121, 2018, ISSN: 0306-2619.
Abstract | Links | BibTeX | Tags: Analogue method, Coral Reefs Optimization, Representative points, Wind power, Wind power field reconstruction
@article{SALCEDOSANZ20181111,
title = {Wind power field reconstruction from a reduced set of representative measuring points},
author = {Sancho Salcedo-Sanz and R. García-Herrera and C. Camacho-Gómez and A. Aybar-Ruíz and E. Alexandre},
url = {https://www.sciencedirect.com/science/article/pii/S0306261918310304},
doi = {https://doi.org/10.1016/j.apenergy.2018.07.003},
issn = {0306-2619},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Applied Energy},
volume = {228},
pages = {1111-1121},
abstract = {In this paper we deal with a problem of representative measuring points selection for long-term wind power analysis. It has direct applications such as wind farm prospective location or long-term power generation prediction in wind-based energy facilities. The problem’s objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a wind power error reconstruction measure is minimized, considering a monthly average wind power field. In order to solve this problem, we use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer, which is an evolutionary-type method able to combine different search procedures within a single population. The CRO-SL is hybridized with the Analogue Method as wind power reconstruction method, to identify the most representative points for the wind field. The proposed approach has been tested in the reconstruction of monthly average wind power fields in Europe, from reanalysis data (ERA-Interim reanalysis). The method exhibits strong performance as evidenced from the experiments carried out. The solutions obtained show that the more significant measuring points are mainly located over the Atlantic ocean, which is consistent with the wind speed climatology of the Northern hemisphere mid-latitudes. We have also analyzed the set of least representative points to reconstruct the wind power field (less informative points for whole reconstruction of the field), obtaining points mainly located at the North of Scandinavia (which may be associated with the circumpolar circulation), and some points in the Eastern Mediterranean, which seem to be related to the Etesian winds. Reconstructions at seasonal scales show similar results, which provides confidence on the robustness of the proposed method. The proposed methodology can be further applied to alternative energy-related problems, such as the selection of critical energy infra-structures or the selection of critical points for climate change studies, among others.},
keywords = {Analogue method, Coral Reefs Optimization, Representative points, Wind power, Wind power field reconstruction},
pubstate = {published},
tppubtype = {article}
}
In this paper we deal with a problem of representative measuring points selection for long-term wind power analysis. It has direct applications such as wind farm prospective location or long-term power generation prediction in wind-based energy facilities. The problem’s objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a wind power error reconstruction measure is minimized, considering a monthly average wind power field. In order to solve this problem, we use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer, which is an evolutionary-type method able to combine different search procedures within a single population. The CRO-SL is hybridized with the Analogue Method as wind power reconstruction method, to identify the most representative points for the wind field. The proposed approach has been tested in the reconstruction of monthly average wind power fields in Europe, from reanalysis data (ERA-Interim reanalysis). The method exhibits strong performance as evidenced from the experiments carried out. The solutions obtained show that the more significant measuring points are mainly located over the Atlantic ocean, which is consistent with the wind speed climatology of the Northern hemisphere mid-latitudes. We have also analyzed the set of least representative points to reconstruct the wind power field (less informative points for whole reconstruction of the field), obtaining points mainly located at the North of Scandinavia (which may be associated with the circumpolar circulation), and some points in the Eastern Mediterranean, which seem to be related to the Etesian winds. Reconstructions at seasonal scales show similar results, which provides confidence on the robustness of the proposed method. The proposed methodology can be further applied to alternative energy-related problems, such as the selection of critical energy infra-structures or the selection of critical points for climate change studies, among others.