F. Antonanzas-Torres

Improving daily output of global to direct solar irradiance models with ground measurements

A comparison of the DirIndex model for computing direct normal solar irradiance from global horizontal using different clear sky transmittance models is presented for four ground stations that belong to Baseline Surface Radiation Network (BSRN) and Aerosol RObotic NETwork (AERONET) networks. The results of DirInt model, which does not include any clear sky transmittance contribution, are also shown. The input for the different clear sky models selected (European Solar Radiation Atlas (ESRA), SOLar Irradiance Scheme (SOLIS) simplified, and Reference Evaluation of Solar Transmittance, 2 bands (REST2)) was generated from the original aerosol optical depth and water vapour measurements provided by AERONET. The results show different trends in the performance of the DirIndex model combined with the clear sky methods. An attempt to correct the trends to the proper one is finally proposed here and the improvement achieved is shown.

Estimation of solar global irradiation in remote areas

Solar global irradiation is barely recorded in remote areas around the world. The lack of access to an electricity grid in these areas presents an enormous opportunity for electrification through renewable energy sources and, specifically, with photovoltaic energy where great solar resources are available. Traditionally, solar resource estimation was performed using parametric-empirical models based on the relationship between solar irradiation and other atmospheric and commonly measured variables, such as temperatures, rainfall, sunshine duration, etc., achieving a relatively high level of certainty. The significant improvement in soft-computing techniques, applied extensively in many research fields, has led to improvements in solar global irradiation modeling. This study conducts a comparative assessment of four different soft-computing techniques (artificial neural networks, support vector regression, M5P regression trees, and extreme learning machines). The results were also compared with two well-kn...

Downscaling of global solar irradiation in complex areas in R

A methodology for downscaling solar irradiation from satellite-derived databases is described using R software. Different packages such as raster, parallel, solaR, gstat, sp, and rasterVis are considered in this study for improving solar resource estimation in areas with complex topography, in which downscaling is a very useful tool for reducing inherent deviations in satellite-derived irradiation databases, which lack of high global spatial resolution. A topographical analysis of horizon blocking and sky-view is developed with a digital elevation model to determine what fraction of hourly solar irradiation reaches the Earths surface. Eventually, kriging with external drift is applied for a better estimation of solar irradiation throughout the region analyzed including the use of local measurements. This methodology has been implemented as an example within the region of La Rioja in northern Spain. The mean absolute error found using the methodology proposed is 91.92 kW h/m2 vs. 172.62 kW h/m2 using the ...

GA-PARSIMONY

Graphical abstractDisplay Omitted HighlightsGA-PARSIMONY combines feature selection and model parameter optimization.Selection of best parsimonious models according to cost and complexity separately.Lower number of features selected in 65% of 20 UCI and Statlib databases tested.GA-PARSIMONY proved useful in SVR control models for a hot dip galvanizing line. This article proposes a new genetic algorithm (GA) methodology to obtain parsimonious support vector regression (SVR) models capable of predicting highly precise setpoints in a continuous annealing furnace (GA-PARSIMONY). The proposal combines feature selection, model tuning, and parsimonious model selection in order to achieve robust SVR models. To this end, a novel GA selection procedure is introduced based on separate cost and complexity evaluations. The best individuals are initially sorted by an error fitness function, and afterwards, models with similar costs are rearranged according to model complexity measurement so as to foster models of lesser complexity. Therefore, the user-supplied penalty parameter, utilized to balance cost and complexity in other fitness functions, is rendered unnecessary. GA-PARSIMONY performed similarly to classical GA on twenty benchmark datasets from public repositories, but used a lower number of features in a striking 65% of models. Moreover, the performance of our proposal also proved useful in a real industrial process for predicting three temperature setpoints for a continuous annealing furnace. The results demonstrated that GA-PARSIMONY was able to generate more robust SVR models with less input features, as compared to classical GA.

Extensive validation of CM SAF surface radiation products over Europe

This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products. The quality of the pyranometer was the most critical source of uncertainty identified. In this respect, the use of records from Second Class pyranometers and silicon-based photodiodes increased the absolute error and the bias, as well as the dispersion of both metrics, preventing an adequate validation of the daily means. The best spatial estimates for the three datasets were obtained in Central Europe with a Mean Absolute Deviation (MAD) within 8–13 W/m2, whereas the MAD always increased at high-latitudes, snow-covered surfaces, high mountain ranges and coastal areas. Overall, the SARAH-JRCs accuracy was demonstrated over a dense network of stations making it the most consistent dataset for climate monitoring applications. The operational dataset was comparable to SARAH-JRC in Central Europe, but lacked of the temporal stability of climate datasets, while CLARA-A2 did not achieve the same level of accuracy despite predictions obtained showed high uniformity with a small negative bias. The ERA-Interim reanalysis shows the by-far largest deviations from the surface reference measurements.

Parsimonious Support Vector Machines Modelling for Set Points in Industrial Processes Based on Genetic Algorithm Optimization

An optimization based on genetic algorithms for both feature selection and model tuning is presented to improve the prediction of set points in industrial lines. The objective is the development of an automatic procedure that efficiently generates parsimonious prediction models with higher generalisation capacity. These models can achieve higher accuracy in predictions, maintaining the high quality of products while working with continual changes in the production cycle. The proposed method deals with three strict restrictions: few individuals per population, low number of holds and runs in model validation procedure and a reduced number of maximum generations. To fullfill these restrictions, we propose to include in the optimization the reranking of the individuals by their complexity when no significant difference is found between the values of their fitness functions. The method is applied to develop support vector machines for predicting three temperature set points in the annealing furnace of a continuous hot-dip galvanising line. The results demonstrate the rerank makes more efficiently and easily the process of obtaining parsimonious models without reducing performance.

Overall models based on ensemble methods for predicting continuous annealing furnace temperature settings

Abstract The prediction of the set points for continuous annealing furnaces on hot dip galvanising lines is essential if high product quality is to be maintained and energy consumption and related emissions into the atmosphere are to be reduced. Owing to the global and evolving nature of the galvanising industry, plant engineers are currently demanding better overall prediction models that maintain accuracy while working with continual changes in the production cycle. This paper presents three promising prediction models based on ensemble methods (additive regression, bagging and dagging) and compares them with models based on artificial intelligence to highlight how good ensembles are at creating overall models with lower generalisation errors. The models are trained using coil properties, chemical compositions of the steel and historical data from a galvanising process operating in Spain. The results show that the potential benefits from such ensemble models, once configured properly, include high performance in terms of both prediction and generalisation capacity, as well as reliability in prediction and a significant reduction in the difficulty of setting up the model.

Evaluation of a novel GA-based methodology for model structure selection: The GA-PARSIMONY

Abstract Most proposed metaheuristics for feature selection and model parameter optimization are based on a two-termed L o s s + P e n a l t y function. Their main drawback is the need of a manual set of the parameter that balances between the loss and the penalty term. In this paper, a novel methodology referred as the GA-PARSIMONY and specifically designed to overcome this issue is evaluated in detail in thirteen public databases with five regression techniques. It is a GA-based meta-heuristic that splits the classic two-termed minimization functions by making two consecutive ranks of individuals. The first rank is based solely on the generalization error, while the second (named ReRank) is based on the complexity of the models, giving a special weight to the complexity entailed by large number of inputs. For each database, models with lowest testing RMSE and without statistical difference among them were referred as winner models. Within this group, the number of features selected was below 50%, which proves an optimal balance between error minimization and parsimony. Particularly, the most complex algorithms (MLP and SVR) were mostly selected in the group of winner models, while using around40–45% of the available attributes. The most basic IBk, ridge regression (LIN) and M5P were only classified as winner models in the simpler databases, but using less number of features in those cases (up to a 20–25% of the initial inputs).

Estimation of Daily Global Horizontal Irradiation Using Extreme Gradient Boosting Machines

Empirical models are widely used to estimate solar radiation at locations where other more readily available meteorological variables are recorded. Within this group, soft computing techniques are the ones that provide more accurate results as they are able to relate all recorded variables with solar radiation. In this work, a new implementation of Gradient Boosting Machines (GBMs) named XGBoost is used to predict daily global horizontal irradiation at locations where no pyranometer records are available. The study is conducted with data from 38 ground stations in Castilla-La Mancha from 2001 to 2013.

Benchmark of algorithms for solar clear-sky detection

Nowadays, solar resource estimation via clear-sky models is widely accepted when correctly validated with on ground records. In the past, different approaches have been proposed in order to determine clear-sky periods of solar radiation on-ground records: visual inspection of registers, discretization via a threshold value of clear sky index, and correlation with estimated clear sky solar irradiation. However, due to the fact that the process must be automated and the need for universality, the search for clear-sky conditions presents a challenging feat. This study proposes a new algorithm based on the persistent value of the Linke turbidity in conjunction with a transitory filter. The determinant of the correlation matrix of estimated clear-sky solar irradiance and measured irradiance is calculated to distinguish between days under clear-sky conditions and cloudy or overcast days. The method was compared and proved superior against a review of other 10 commonly used techniques at 21 sites of the Baseline Surface Radiation Network, which includes diverse climates and terrain.