Luis Prieto - Profile on Academia.edu (original) (raw)
Papers by Luis Prieto
Applied Soft Computing, 2022
Randomization-based Machine Learning methods for prediction are currently a hot topic in Artifici... more Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based approaches to renewable energy prediction problems has been massive in the last few years, including many different types of randomization-based approaches, their hybridization with other techniques and also the description of new versions of classical randomization-based algorithms, including deep and ensemble approaches. In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We describe the most important methods and algorithms of this family of modeling methods, and perform a critical literature review, examining predic
This paper proposes a neural network model for wind speed prediction, a very important task in wi... more This paper proposes a neural network model for wind speed prediction, a very important task in wind parks management. Currently, several physicalstatistical and artificial intelligence (AI) wind speed prediction models are used to this end. A recently proposed hybrid model is based on hybridizations of global and mesoscale forecasting systems, with a final downscaling step using a multilayer perceptron (MLP). In this paper, we test an alternative neural model for this final step of downscaling, in which projection hyperbolic tangent units (HTUs) are used within feed forward neural networks. The architecture, weights and node typology of the HTU-based network are learnt using a hybrid evolutionary programming algorithm. This new methodology is tested over a real problem of wind speed forecasting, in which we show that our method is able to improve the performance of previous MLPs, obtaining an interpretable model of final regression for each turbine in the wind park.
Open Computer Science, 2011
This paper presents a mini-review of the main works recently published about optimal wind turbine... more This paper presents a mini-review of the main works recently published about optimal wind turbines layout in wind farms. Specifically, we focus on discussing articles where evolutionary computation techniques have been applied, since this computational framework has obtained very good results in different formulations of the problem. A summary of the main concepts needed to face the problem are also included in the article, such as a basic wake model and several cost models and objective functions previously used in the literature. This review includes works published in the most significant journals and international conferences, and it gives a brief remark of the optimization models proposed and the implemented algorithms, so it can be useful for readers who want to be quickly introduced in this research area.
Energy, 2011
In this paper we present an evolutionary approach for the problem of discovering pressure pattern... more In this paper we present an evolutionary approach for the problem of discovering pressure patterns under a quality measure related to wind speed and direction. This clustering problem is specially interesting for companies involving in the management of wind farms, since it can be useful for analysis of results of the wind farm in a given period and also for long-term wind speed prediction. The proposed evolutionary algorithm is based on a specific encoding of the problem, which uses a dimensional reduction of the problem. With this special encoding, the required centroids are evolved together with some other parameters of the algorithm. We define a specific crossover operator and two different mutations in order to improve the evolutionary search of the proposed approach. In the experimental part of the paper, we test the performance of our approach in a real problem of pressure pattern extraction in the Iberian Peninsula, using a wind speed and direction series in a wind farm in the center of Spain. We compare the performance of the proposed evolutionary algorithm with that of an existing weather types (WT) purely meteorological approach, and we show that the proposed evolutionary approach is able to obtain better results than the WT approach.
Energy Conversion and Management, 2014
This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs ... more This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks' training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO-ELM approach in this FSP wind speed prediction problem.
Renewable Energy, 2015
This paper evaluates the performance of different types of Regression Trees (RTs) in a real probl... more 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.
Applied Energy, 2012
This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressur... more This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressure patterns. The algorithm operates in a search space formed by grids of pressure measures, and must classify the different situations into classes, in such a way that a measure of wind speed in a given point is minimized among patterns assigned to the same class. Then, each class is assigned a mean wind speed and direction, so the wind speed reconstruction is possible for a new grid of synoptic pressures. In this paper we present the problem model and the specific description of the evolutionary algorithm proposed to solve the problem. We also show the good performance of the proposed method in the reconstruction of the average wind speed in six wind towers in Spain. The proposed method is applicable to wind speed reconstruction or reconstruction of wind missing data of wind series, specially when there is no other variable or related measure available.
Neural Computing and Applications, 2012
We propose a classification method based on a special class of feedforward neural network, namely... more We propose a classification method based on a special class of feedforward neural network, namely product-unit neural networks. They are based on multiplicative nodes instead of additive ones, where the nonlinear basis functions express the possible strong interactions between variables. We apply an evolutionary algorithm to determine the basic structure of the product-unit model and to estimate the coefficients of the model. We use softmax transformation as the decision rule and the cross-entropy error function because of its probabilistic interpretation. The empirical results over four benchmark data sets show that the proposed model is very promising in terms of classification accuracy and the complexity of the classifier, yielding a state-ofthe-art performance.
International Journal of Climatology, 2014
The study of the wind power output variability comprises different time scales. Among these, low-... more The study of the wind power output variability comprises different time scales. Among these, low-frequency variations can substantially modify the performance of a wind power plant during its lifetime. In recent years other temporal scales, such as the short-term variability or the climatological conditions of wind and the corresponding generated power have been investigated in depth. However, the study of longer-decadal and multidecadal-variations is still in its early stages. In this work the wind power output long-term variability is analyzed for two locations in Spain, during the period 1871-2009. This is attained by computing the annual wind speed Probability Density Functions (PDF) derived from an ensemble of atmospheric Sea Level Pressure (SLP) data set through a statistical downscaling based in Evolutionary Algorithms. Results reveal significant trends and periodicities in multidecadal bands including 13, 25 and 46 years, as well as significant differences among both sites. The impact of the leading large-scale circulation patterns (NAO, EA, SCAND and AMO) on wind power output and its stationarity is analyzed. Results on both locations show non-stationary significant and opposite seasonal couplings with these forcings. Finally, the long-term variability of the reconstructed Weibull parameters of the annual wind speed distributions are employed to derive a linear model to estimate the annual wind power.
Short-Term Wind Speed Prediction by Hybridizing Global and Mesoscale Forecasting Models with Artificial Neural Networks
2008 Eighth International Conference on Hybrid Intelligent Systems, 2008
... A. Portilla-Figueras , L. Prieto ∗ , D. Paredes and F. Correoso ∗ ... REFERENCES [1] MCMa... more ... A. Portilla-Figueras , L. Prieto ∗ , D. Paredes and F. Correoso ∗ ... REFERENCES [1] MCMabel and E. Fernández, Analysis of wind power generation and prediction using ANN: A case study, Renewable Energy, vol. 33, no. 5, pp. 986-992, 2008. ...
Evaluating nominal and ordinal classifiers for wind speed prediction from synoptic pressure patterns
A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction
Renewable Energy, 2015
ABSTRACT This paper introduces a new hybrid bio-inspired solver which combines elements from the ... more ABSTRACT This paper introduces a new hybrid bio-inspired solver which combines elements from the recently proposed Coral Reefs Optimization (CRO) algorithm with operators from the Harmony Search (HS) approach, which gives rise to the coined CRO-HS optimization technique. Specifically, this novel bio-inspired optimizer is utilized in the context of short-term wind speed prediction as a means to obtain the best set of meteorological variables to be input to a neural Extreme Learning Machine (ELM) network. The paper elaborates on the main characteristics of the proposed scheme and discusses its performance when predicting the wind speed based on the measures of two meteorological towers located in USA and Spain. The good results obtained in these experiments when compared to naïve versions of the CRO and HS algorithms are promising and pave the way towards the utilization of the derived hybrid solver in other optimization problems arising from diverse disciplines.
Neurocomputing, 2009
Wind speed prediction is a very important part of wind parks management. Currently, hybrid physic... more Wind speed prediction is a very important part of wind parks management. Currently, hybrid physicalstatistical wind speed forecasting models are used to this end, some of them using neural networks as the final step to obtain accurate wind speed predictions. In this paper we propose a method to improve the performance of one of these hybrid systems, by exploiting diversity in the input data of the neural network part of the system. The diversity in the data is produced by the physical models of the system, applied with different parameterizations. Two structures of neural network banks are used to exploit the input data diversity. We will show that our method is able to improve the performance of the system, obtaining accurate wind speed predictions better than the one obtained by the system using single neural networks.
Meteorology and Atmospheric Physics, 2005
This paper characterizes Mesoscale Convective Systems (MCSs) during 2001 over Iberia and the Bale... more This paper characterizes Mesoscale Convective Systems (MCSs) during 2001 over Iberia and the Balearic Islands and their meteorological settings. Enhanced infrared Meteosat imagery has been used to detect their occurrence over the Western Mediterranean region between June and December 2001 according to satellite-defined criteria based on the MCS physical characteristics. Twelve MCSs have been identified. The results show that the occurrence of 2001 MCSs is limited to the August-October period, with September being the most active period. They tend to develop during the late afternoon or early night, with preferred eastern Iberian coast locations and eastward migrations. A cloud shield area of 50.000 km 2 is rarely exceeded. When our results are compared with previous studies, it is possible to assert that though 2001 MCS activity was moderate, the convective season was substantially less prolonged than usual, with shorter MCS life cycles and higher average speeds. The average MCS precipitation rate was 3.3 mm Á h À1 but a wide range of values varying from scarce precipitation to intense events of 130 mm Á 24 h À1 (6 September) were collected. The results suggest that, during 2001, MCS rainfall was the principal source of precipitation in the Mediterranean region during the convective season, but its impact varied according to the location. Synoptic analysis based on NCEP=NCAR reanalysis show that several common precursors could be identified over the Western Mediterranean Sea when the 2001 MCSs occurred: a low-level tongue of moist air and precipitable water (PW) exceeding 25 mm through the southern portion of the Western Mediterranean area, low-level zonal warm advection over 2 C Á 24 h À1 towards eastern Iberia, a modest 1000-850 hPa equivalent potential temperature (e) difference over 20 C located close to the eastern Iberian coast, a mid level trough (sometimes a cutoff low) over Northern Africa or Southern Spain and high levels geostrophic vorticity advection exceeding 12 Á 10 À10 s À2 over eastern Iberia and Northern Africa. Finally, the results suggest that synoptic, orographic and a warm-air advection were the most relevant forcing mechanisms during 2001.
Heuristic correction of wind speed mesoscale models simulations for wind farms prospecting and micrositing
Journal of Wind Engineering and Industrial Aerodynamics, 2014
Very fast training neural-computation techniques for real measure-correlate-predict wind operations in wind farms
Journal of Wind Engineering and Industrial Aerodynamics, 2013
International Journal of Biometeorology, 2005
During the last few years great attention has been paid to the evaluation of the impact of extrem... more During the last few years great attention has been paid to the evaluation of the impact of extreme temperatures on human health. This paper examines the effect of extreme winter temperature on mortality in Madrid for people older than 65, using ARIMA and GAM models. Data correspond to 1,815 winter days over the period 1986-1997, during which time a total of 133,000 deaths occurred. The daily maximum temperature (T max) was shown to be the best thermal indicator of the impact of climate on mortality. When total mortality was considered, the maximum impact occured 7-8 days after a temperature extreme; for circulatory diseases the lag was between 7 and 14 days. When respiratory causes were considered, two mortality peaks were evident at 4-5 and 11 days. When the impact of winter extreme temperatures was compared with that associated with summer extremes, it was found to occur over a longer term, and appeared to be more indirect.
Evolutionary computation approaches for real offshore wind farm layout: A case study in northern Europe
Expert Systems with Applications, 2013
ABSTRACT This paper presents the layout optimization of a real offshore wind farm in northern Eur... more ABSTRACT This paper presents the layout optimization of a real offshore wind farm in northern Europe, using evolutionary computation techniques. Different strategies for the wind farm design are tested, such as regular turbines layout or free turbines disposition with fixed number of turbines. Also, different layout quality models have been applied, in order to obtain solutions with different characteristics of high energy production and low interlink cost. In all the cases, evolutionary algorithms are developed and detailed in the paper. The experiments carried out in the real problem show that the free design with fixed number of turbines is more appropriate and obtains better quality layouts than the regular design.
NAO influence on extreme winter temperatures in Madrid (Spain)
Annales Geophysicae, 2002
Synoptic conditions leading to extremely high temperatures in Madrid
Annales Geophysicae, 2002
Applied Soft Computing, 2022
Randomization-based Machine Learning methods for prediction are currently a hot topic in Artifici... more Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based approaches to renewable energy prediction problems has been massive in the last few years, including many different types of randomization-based approaches, their hybridization with other techniques and also the description of new versions of classical randomization-based algorithms, including deep and ensemble approaches. In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We describe the most important methods and algorithms of this family of modeling methods, and perform a critical literature review, examining predic
This paper proposes a neural network model for wind speed prediction, a very important task in wi... more This paper proposes a neural network model for wind speed prediction, a very important task in wind parks management. Currently, several physicalstatistical and artificial intelligence (AI) wind speed prediction models are used to this end. A recently proposed hybrid model is based on hybridizations of global and mesoscale forecasting systems, with a final downscaling step using a multilayer perceptron (MLP). In this paper, we test an alternative neural model for this final step of downscaling, in which projection hyperbolic tangent units (HTUs) are used within feed forward neural networks. The architecture, weights and node typology of the HTU-based network are learnt using a hybrid evolutionary programming algorithm. This new methodology is tested over a real problem of wind speed forecasting, in which we show that our method is able to improve the performance of previous MLPs, obtaining an interpretable model of final regression for each turbine in the wind park.
Open Computer Science, 2011
This paper presents a mini-review of the main works recently published about optimal wind turbine... more This paper presents a mini-review of the main works recently published about optimal wind turbines layout in wind farms. Specifically, we focus on discussing articles where evolutionary computation techniques have been applied, since this computational framework has obtained very good results in different formulations of the problem. A summary of the main concepts needed to face the problem are also included in the article, such as a basic wake model and several cost models and objective functions previously used in the literature. This review includes works published in the most significant journals and international conferences, and it gives a brief remark of the optimization models proposed and the implemented algorithms, so it can be useful for readers who want to be quickly introduced in this research area.
Energy, 2011
In this paper we present an evolutionary approach for the problem of discovering pressure pattern... more In this paper we present an evolutionary approach for the problem of discovering pressure patterns under a quality measure related to wind speed and direction. This clustering problem is specially interesting for companies involving in the management of wind farms, since it can be useful for analysis of results of the wind farm in a given period and also for long-term wind speed prediction. The proposed evolutionary algorithm is based on a specific encoding of the problem, which uses a dimensional reduction of the problem. With this special encoding, the required centroids are evolved together with some other parameters of the algorithm. We define a specific crossover operator and two different mutations in order to improve the evolutionary search of the proposed approach. In the experimental part of the paper, we test the performance of our approach in a real problem of pressure pattern extraction in the Iberian Peninsula, using a wind speed and direction series in a wind farm in the center of Spain. We compare the performance of the proposed evolutionary algorithm with that of an existing weather types (WT) purely meteorological approach, and we show that the proposed evolutionary approach is able to obtain better results than the WT approach.
Energy Conversion and Management, 2014
This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs ... more This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks' training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO-ELM approach in this FSP wind speed prediction problem.
Renewable Energy, 2015
This paper evaluates the performance of different types of Regression Trees (RTs) in a real probl... more 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.
Applied Energy, 2012
This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressur... more This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressure patterns. The algorithm operates in a search space formed by grids of pressure measures, and must classify the different situations into classes, in such a way that a measure of wind speed in a given point is minimized among patterns assigned to the same class. Then, each class is assigned a mean wind speed and direction, so the wind speed reconstruction is possible for a new grid of synoptic pressures. In this paper we present the problem model and the specific description of the evolutionary algorithm proposed to solve the problem. We also show the good performance of the proposed method in the reconstruction of the average wind speed in six wind towers in Spain. The proposed method is applicable to wind speed reconstruction or reconstruction of wind missing data of wind series, specially when there is no other variable or related measure available.
Neural Computing and Applications, 2012
We propose a classification method based on a special class of feedforward neural network, namely... more We propose a classification method based on a special class of feedforward neural network, namely product-unit neural networks. They are based on multiplicative nodes instead of additive ones, where the nonlinear basis functions express the possible strong interactions between variables. We apply an evolutionary algorithm to determine the basic structure of the product-unit model and to estimate the coefficients of the model. We use softmax transformation as the decision rule and the cross-entropy error function because of its probabilistic interpretation. The empirical results over four benchmark data sets show that the proposed model is very promising in terms of classification accuracy and the complexity of the classifier, yielding a state-ofthe-art performance.
International Journal of Climatology, 2014
The study of the wind power output variability comprises different time scales. Among these, low-... more The study of the wind power output variability comprises different time scales. Among these, low-frequency variations can substantially modify the performance of a wind power plant during its lifetime. In recent years other temporal scales, such as the short-term variability or the climatological conditions of wind and the corresponding generated power have been investigated in depth. However, the study of longer-decadal and multidecadal-variations is still in its early stages. In this work the wind power output long-term variability is analyzed for two locations in Spain, during the period 1871-2009. This is attained by computing the annual wind speed Probability Density Functions (PDF) derived from an ensemble of atmospheric Sea Level Pressure (SLP) data set through a statistical downscaling based in Evolutionary Algorithms. Results reveal significant trends and periodicities in multidecadal bands including 13, 25 and 46 years, as well as significant differences among both sites. The impact of the leading large-scale circulation patterns (NAO, EA, SCAND and AMO) on wind power output and its stationarity is analyzed. Results on both locations show non-stationary significant and opposite seasonal couplings with these forcings. Finally, the long-term variability of the reconstructed Weibull parameters of the annual wind speed distributions are employed to derive a linear model to estimate the annual wind power.
Short-Term Wind Speed Prediction by Hybridizing Global and Mesoscale Forecasting Models with Artificial Neural Networks
2008 Eighth International Conference on Hybrid Intelligent Systems, 2008
... A. Portilla-Figueras , L. Prieto ∗ , D. Paredes and F. Correoso ∗ ... REFERENCES [1] MCMa... more ... A. Portilla-Figueras , L. Prieto ∗ , D. Paredes and F. Correoso ∗ ... REFERENCES [1] MCMabel and E. Fernández, Analysis of wind power generation and prediction using ANN: A case study, Renewable Energy, vol. 33, no. 5, pp. 986-992, 2008. ...
Evaluating nominal and ordinal classifiers for wind speed prediction from synoptic pressure patterns
A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction
Renewable Energy, 2015
ABSTRACT This paper introduces a new hybrid bio-inspired solver which combines elements from the ... more ABSTRACT This paper introduces a new hybrid bio-inspired solver which combines elements from the recently proposed Coral Reefs Optimization (CRO) algorithm with operators from the Harmony Search (HS) approach, which gives rise to the coined CRO-HS optimization technique. Specifically, this novel bio-inspired optimizer is utilized in the context of short-term wind speed prediction as a means to obtain the best set of meteorological variables to be input to a neural Extreme Learning Machine (ELM) network. The paper elaborates on the main characteristics of the proposed scheme and discusses its performance when predicting the wind speed based on the measures of two meteorological towers located in USA and Spain. The good results obtained in these experiments when compared to naïve versions of the CRO and HS algorithms are promising and pave the way towards the utilization of the derived hybrid solver in other optimization problems arising from diverse disciplines.
Neurocomputing, 2009
Wind speed prediction is a very important part of wind parks management. Currently, hybrid physic... more Wind speed prediction is a very important part of wind parks management. Currently, hybrid physicalstatistical wind speed forecasting models are used to this end, some of them using neural networks as the final step to obtain accurate wind speed predictions. In this paper we propose a method to improve the performance of one of these hybrid systems, by exploiting diversity in the input data of the neural network part of the system. The diversity in the data is produced by the physical models of the system, applied with different parameterizations. Two structures of neural network banks are used to exploit the input data diversity. We will show that our method is able to improve the performance of the system, obtaining accurate wind speed predictions better than the one obtained by the system using single neural networks.
Meteorology and Atmospheric Physics, 2005
This paper characterizes Mesoscale Convective Systems (MCSs) during 2001 over Iberia and the Bale... more This paper characterizes Mesoscale Convective Systems (MCSs) during 2001 over Iberia and the Balearic Islands and their meteorological settings. Enhanced infrared Meteosat imagery has been used to detect their occurrence over the Western Mediterranean region between June and December 2001 according to satellite-defined criteria based on the MCS physical characteristics. Twelve MCSs have been identified. The results show that the occurrence of 2001 MCSs is limited to the August-October period, with September being the most active period. They tend to develop during the late afternoon or early night, with preferred eastern Iberian coast locations and eastward migrations. A cloud shield area of 50.000 km 2 is rarely exceeded. When our results are compared with previous studies, it is possible to assert that though 2001 MCS activity was moderate, the convective season was substantially less prolonged than usual, with shorter MCS life cycles and higher average speeds. The average MCS precipitation rate was 3.3 mm Á h À1 but a wide range of values varying from scarce precipitation to intense events of 130 mm Á 24 h À1 (6 September) were collected. The results suggest that, during 2001, MCS rainfall was the principal source of precipitation in the Mediterranean region during the convective season, but its impact varied according to the location. Synoptic analysis based on NCEP=NCAR reanalysis show that several common precursors could be identified over the Western Mediterranean Sea when the 2001 MCSs occurred: a low-level tongue of moist air and precipitable water (PW) exceeding 25 mm through the southern portion of the Western Mediterranean area, low-level zonal warm advection over 2 C Á 24 h À1 towards eastern Iberia, a modest 1000-850 hPa equivalent potential temperature (e) difference over 20 C located close to the eastern Iberian coast, a mid level trough (sometimes a cutoff low) over Northern Africa or Southern Spain and high levels geostrophic vorticity advection exceeding 12 Á 10 À10 s À2 over eastern Iberia and Northern Africa. Finally, the results suggest that synoptic, orographic and a warm-air advection were the most relevant forcing mechanisms during 2001.
Heuristic correction of wind speed mesoscale models simulations for wind farms prospecting and micrositing
Journal of Wind Engineering and Industrial Aerodynamics, 2014
Very fast training neural-computation techniques for real measure-correlate-predict wind operations in wind farms
Journal of Wind Engineering and Industrial Aerodynamics, 2013
International Journal of Biometeorology, 2005
During the last few years great attention has been paid to the evaluation of the impact of extrem... more During the last few years great attention has been paid to the evaluation of the impact of extreme temperatures on human health. This paper examines the effect of extreme winter temperature on mortality in Madrid for people older than 65, using ARIMA and GAM models. Data correspond to 1,815 winter days over the period 1986-1997, during which time a total of 133,000 deaths occurred. The daily maximum temperature (T max) was shown to be the best thermal indicator of the impact of climate on mortality. When total mortality was considered, the maximum impact occured 7-8 days after a temperature extreme; for circulatory diseases the lag was between 7 and 14 days. When respiratory causes were considered, two mortality peaks were evident at 4-5 and 11 days. When the impact of winter extreme temperatures was compared with that associated with summer extremes, it was found to occur over a longer term, and appeared to be more indirect.
Evolutionary computation approaches for real offshore wind farm layout: A case study in northern Europe
Expert Systems with Applications, 2013
ABSTRACT This paper presents the layout optimization of a real offshore wind farm in northern Eur... more ABSTRACT This paper presents the layout optimization of a real offshore wind farm in northern Europe, using evolutionary computation techniques. Different strategies for the wind farm design are tested, such as regular turbines layout or free turbines disposition with fixed number of turbines. Also, different layout quality models have been applied, in order to obtain solutions with different characteristics of high energy production and low interlink cost. In all the cases, evolutionary algorithms are developed and detailed in the paper. The experiments carried out in the real problem show that the free design with fixed number of turbines is more appropriate and obtains better quality layouts than the regular design.
NAO influence on extreme winter temperatures in Madrid (Spain)
Annales Geophysicae, 2002
Synoptic conditions leading to extremely high temperatures in Madrid
Annales Geophysicae, 2002