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Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage.

Deep neural networks (DNN) are becoming increasingly relevant for probabilistic forecasting because of their ability to estimate prediction intervals (PIs). Two different ways for estimating PIs with neural networks stand out: quantile estimation for posterior PI construction and direct PI estimation. The former first estimates quantiles, which are then used to construct PIs, while the latter directly obtains the lower and upper PI bounds by optimizing some loss functions, with the advantage that PI width is directly considered in the optimization process and thus may result in narrower intervals. In this work, two different DNN-based models are studied for direct PI estimation, and compared with DNN for quantile estimation in the context of solar and wind regional energy forecasting. The first approach is based on the recent quality-driven loss and is formulated to estimate multiple PIs with a single model. The second is a novel approach that employs hypernetworks (HN), where direct PI estimation is formulated as a multi-objective problem, returning a Pareto front of solutions that contains all possible coverage-width optimal trade-offs. This formulation allows HN to obtain optimal PIs for all possible coverages without increasing the number of network outputs or adjusting additional hyperparameters, as opposed to the first direct model. Results show that prediction intervals from direct estimation are narrower (up to 20%) than those of quantile estimation, for target coverages 70%–80% for all regions, and also 85%, 90%, and 95% depending on the region, while HN always achieves the required coverage for the higher target coverages. This publication is part of the I+D+i project PID2019-107455RBC22, funded by MCIN /AEI/10.13039/501100011033. This work was also supported by the Comunidad de Madrid Excellence Program. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022)

In the last years, there is an increasing interest for enhanced method for assessing and monitoring the level of the global horizontal irradiance (GHI) in photovoltaic (PV) systems, fostered by the massive deployment of this energy. Thermopile or photodiode pyranometers provide point measurements, which may not be adequate in cases when areal information is important (as for PV network or large PV plants monitoring). The use of All Sky Imagers paired convolutional neural networks, a powerful technique for estimation, has been proposed as a plausible alternative. In this work, a convolutional neural network architecture is presented to estimate solar irradiance from sets of ground-level Total Sky Images. This neural network is capable of combining images from three cameras. Results show that this approach is more accurate than using only images from a single camera. It has also been shown to improve the performance of two other approaches: a cloud fraction model and a feature extraction model. This work has been made possible by the Ministerio de Economia y Empresa of Spain, under the project PROSOL (ENE2014-56126-C2). Authors from the University of Jaen are supported by the Junta de Andalucía (Research group TEP-220) and by FEDER funds. This work has been made possible by projects funded by Agencia Estatal de Investigación (PID2019-107455RB-C21 and PID2019-107455RB-C22 / AEI / 10.13039/501100011033).

In this article we explore the blending of the four models (Satellite, WRF-Solar, Smart Persistence and CIADCast) studied in Part 1 by means of Support Vector Machines with the aim of improving GHI and DNI forecasts. Two blending approaches that use the four models as predictors have been studied: the horizon approach constructs a different blending model for each forecast horizon, while the general approach trains a single model valid for all horizons. The influence on the blending models of adding information about weather types is also studied. The approaches have been evaluated in the same four Iberian Peninsula stations of Part 1. Blending approaches have been extended to a regional context with the goal of obtaining improved regional forecasts. In general, results show that blending greatly outperforms the individual predictors, with no large differences between the blending approaches themselves. Horizon approaches were more suitable to minimize rRMSE and general approaches work better for rMAE. The relative improvement in rRMSE obtained by model blending was up to 17% for GHI (16% for DNI), and up to 15% for rMAE. Similar improvements were observed for the regional forecast. An analysis of performance depending on the horizon shows that while the advantage of blending for GHI remains more or less constant along horizons, it tends to increase with horizon for DNI, with the largest improvements occurring at 6 h. The knowledge of weather conditions helped to slightly improve further the forecasts (up to 3%), but only at some locations and for rRMSE. The authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R (http://prosol.uc3m.es). The University of Jan team are also supported by FEDER funds and by the Junta de Andalucia (Research group TEP-220).The authors are in debt with the National Centers for Environmental Prediction (NCEP), EUMETSAT, Faculdade de Ciencias da Universidade de Lisboa, Grupo de Energa Solar of the Universidad Politcnica de Madrid and Abengoa Solar for providing the data used in this work.

The aim of data transformation is to transform the original feature space of data into another space with better properties. This is typically combined with dimensionality reduction, so that the dimensionality of the transformed space is smaller. A widely used method for data transformation and dimensionality reduction is Principal Component Analysis (PCA). PCA finds a subspace that explains most of the data variance. While the new PCA feature space has interesting properties, such as removing linear correlation, PCA is an unsupervised method. Therefore, there is no guarantee that the PCA feature space will be the most appropriate for supervised tasks, such as classification or regression. On the other hand, 3-layer Multi Layer Perceptrons (MLP), which are supervised methods, can also be understood as a data transformation carried out by the hidden layer, followed by a classification/regression operation performed by the output layer. Given that the hidden layer is obtained after a supervised training process, it can be considered that it is performing a supervised data transformation. And if the number of hidden neurons is smaller than the input, also dimensionality reduction. Despite this kind of transformation being widely available (any neural network package that allows access to the hidden layer weights can be used), no extensive experimentation on the quality of 3-layer MLP data transformation has been carried out. The aim of this article is to carry out this research for classification problems. Results show that, overall, this transformation offers better results than the PCA unsupervised transformation method. This work has been supported by Agencia Estatal de Investigación (PID2019-107455RB-C22 /AEI/ 10.13039/501100011033), and Spanish Ministry of Science and Education under TIN2017-85727-C4-3-P (DeepBio) grant

Prediction Intervals are pairs of lower and upper bounds on point forecasts and are useful to take into account the uncertainty on predictions. This article studies the influence of using measured solar power, available at prediction time, on the quality of prediction intervals. While previous studies have suggested that using measured variables can improve point forecasts, not much research has been done on the usefulness of that additional information, so that prediction intervals with less uncertainty can be obtained. With this aim, a multi-objective particle swarm optimization method was used to train neural networks whose outputs are the interval bounds. The inputs to the network used measured solar power in addition to hourly meteorological forecasts. This study was carried out on data from three different locations and for five forecast horizons, from 1 to 5 h. The results were compared with two benchmark methods (quantile regression and quantile regression forests). The Wilcoxon test was used to assess statistical significance. The results show that using measured power reduces the uncertainty associated to the prediction intervals, but mainly for the short forecasting horizons This work was funded by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R (AOPRIN-SOL project).

Recent research has shown that the integration or blending of different forecasting models is able to improve the predictions of solar radiation. However, most works perform model blending to improve point forecasts, but the integration of forecasting models to improve probabilistic forecasting has not received much attention. In this work the estimation of prediction intervals for the integration of four Global Horizontal Irradiance (GHI) forecasting models (Smart Persistence, WRF-solar, CIADcast, and Satellite) is addressed. Several short-term forecasting horizons, up to one hour ahead, have been analyzed. Within this context, one of the aims of the article is to study whether knowledge about the synoptic weather conditions, which are related to the stability of weather, might help to reduce the uncertainty represented by prediction intervals. In order to deal with this issue, information about which weather type is present at the time of prediction, has been used by the blending model. Four weather types have been considered. A multi-objective variant of the Lower Upper Bound Estimation approach has been used in this work for prediction interval estimation and compared with two baseline methods: Quantile Regression (QR) and Gradient Boosting (GBR). An exhaustive experimental validation has been carried out, using data registered at Seville in the Southern Iberian Peninsula. Results show that, in general, using weather type information reduces uncertainty of prediction intervals, according to all performance metrics used. More specifically, and with respect to one of the metrics (the ratio between interval coverage and width), for high-coverage (0.90, 0.95) prediction intervals, using weather type enhances the ratio of the multi-objective approach by 2%¿. Also, comparing the multi-objective approach versus the two baselines for high-coverage intervals, the improvement is 11%¿% over QR and 10%¿% over GBR. Improvements for low-coverage intervals (0.85) are smaller. The authors are supported by projects funded by Agencia Estatal de Investigación, Spain (PID2019-107455RB-C21 and PID2019-107455RB-C22/AEI/10.13039/501100011033). Also supported by Spanish Ministry of Economy and Competitiveness, project ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R (http://prosol.uc3m.es). The University of Jaén team is also supported by FEDER, Spain funds and by the Junta de Andalucía, Spain (Research group TEP-220)

Based on a large and recently developed database of 1-min irradiance and ancillary data observations at 54 world stations, this study uses the gradient boosting Machine Learning (ML) technique to improve the process of components separation, through which the direct and diffuse solar radiation components are estimated from 1-min global horizontal irradiance data. Here, the XGBoost implementation of gradient boosting is used both with ensembles of linear and ensembles of non-linear weak prediction models. The predictions of 140 separation models of the literature are combined using XGBoost to overall improve the random errors of the predictions of the individual separation models at any of the validation sites. The minimum prediction error is essentially achieved by a combination of 26 out of the original 140 models, with no meaningful reduction in error by combining more models. Most of these 26 models use at least three inputs in addition to clearness index. In parallel, XGBoost is also used to separate the components directly from the inputs to the separation models. From the 24 possible inputs used in the original 140 separation models, only 14 are found relevant. These 14 inputs could be used with appropriate formalism to subsequently develop a better separation model. It is found that when the training and validation datasets are not collocated, the RMSD of the predictions increases, on average, 2% with respect to the case of collocated datasets. Overall, the present results indicate that a data-driven ML approach combining a limited number of existing models can be used to considerably decrease the currently large random errors associated with such models when used separately at high temporal frequency The first two authors have been funded by the Spanish Ministry of Science under project ENE2014-56126-C2-2-R (AOPRIN-SOL project). Jose Antonio Ruiz Arias was supported by the Spanish Ministry of Economy and Competitiveness under the project ENE2014-56126-C2-1-R.

This paper has been presented at: 14th IFIP International Conference on Artificial Intelligence Applications and Innovations In this work two different machine learning approaches have been studied to predict wind power for different time horizons: individual and global models. The individual approach constructs a model for each horizon while the global approach obtains a single model that can be used for all horizons. Both approaches have advantages and disadvantages. Each individual model is trained with data pertaining to a single horizon, thus it can be specific for that horizon, but can use fewer data for training than the global model, which is constructed with data belonging to all horizons. Support Vector Machines have been used for constructing the individual and global models. This study has been tested on energy production data obtained from the Sotavento wind farm and meteorological data from the European Centre for Medium-Range Weather Forecasts, for a 5 × 5 grid around Sotavento. Also, given the large amount of variables involved, a feature selection algorithm (Sequential Forward Selection) has been used in order to improve the performance of the models. Experimental results show that the global model is more accurate than the individual ones, specially when feature selection is used. The authors acknowledge financial support granted by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R.

Image clustering is a critical and essential component of image analysis to several fields and could be considered as an optimization problem. Cuckoo Search (CS) algorithm is an optimization algorithm that simulates the aggressive reproduction strategy of some cuckoo species.In this paper, a combination of CS and classical algorithms (KM, FCM, and KHM) is proposed for unsupervised satellite image classification. Comparisons with classical algorithms and also with CS are performed using three cluster validity indices namely DB, XB, and WB on synthetic and real data sets. Experimental results confirm the effectiveness of the proposed approach.

In this article, a filter feature weighting technique for attribute selection in classification problems is proposed (LIA). It has two main characteristics. First, unlike feature weighting methods, it is able to consider attribute interactions in the weighting process, rather than only evaluating single features. Attribute subsets are evaluated by projecting instances into a grid defined by attributes in the subset. Then, the joint relevance of the subset is computed by measuring the information present in the cells of the grid. The final weight for each attribute is computed by taking into account its performance in each of the grids it participates. Second, many real problems contain low signal-to-noise ratios, due to instance of high noise levels, class overlap, class imbalance, or small training samples. LIA computes reliable local information for each of the cells by estimating the number of target class instances not due to chance, given a confidence value. In order to study its properties, LIA has been evaluated with a collection of 18 real datasets and compared to two feature weighting methods (Chi-Squared and ReliefF) and a subset feature selection algorithm (CFS). Results show that the method is significantly better in many cases, and never significantly worse. LIA has also been tested with different grid dimensions (1, 2, and 3). The method works best when evaluating attribute subsets larger than 1, hence showing the usefulness of considering attribute interactions. The authors acknowledge financial support granted by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R.

This paper has been presented at: 19th Intelligent Data Engineering and Automated Learning (IDEAL 2018) While it is common to make point forecasts for solar energy generation, estimating the forecast uncertainty has received less attention. In this article, prediction intervals are computed within a multi-objective approach in order to obtain an optimal coverage/width tradeoff. In particular, it is studied whether using measured power as an another input, additionally to the meteorological forecast variables, is able to improve the properties of prediction intervals for short time horizons (up to three hours). Results show that they tend to be narrower (i.e. less uncertain), and the ratio between coverage and width is larger. The method has shown to obtain intervals with better properties than baseline Quantile Regression. This work has been funded by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R (AOPRIN-SOL project).

Optimization of channel selection and spatial filter for Brain Computer Interfaces.A multi-objective evolutionary algorithm simultaneously optimizes filter and channels.Multi-objective approach returns a set of solutions (channels vs. error tradeoffs).A simple threshold is encoded to select channels instead of a longer binary mask.Joint optimization of filter and channels performs better than only the channels. Obtaining high accuracy classification from Brain Computer Interfaces require to attach many electrodes on the scalp of subjects. On the other hand, their placement on the scalp involves generally a laborious and time consuming process. Therefore, it is important for the practitioner to estimate how many electrodes, and which ones, are needed to obtain the required accuracy. With this purpose, a multi-objective formulation is proposed in order to obtain a set of solutions (Pareto front) that represent all the optimal tradeoffs between the number of channels and the classification accuracy, from where the practitioner can choose. Additionally, previous research has shown that classification accuracy highly depends on the proper tuning of the filter used to preprocess the electroencephalogram. Therefore, in this work, the Non-dominated Sorting Genetic Algorithm II is used for optimizing both the number of electrodes and the classification error, through the optimization of a spatial filter encoded in the solution. The fact that the filter is part of the solution allows to determine which electrodes are to be selected by using a simple threshold, instead of a long binary mask as in other approaches. Empirical results show that indeed, the simultaneous optimization of the spatial filter and selected electrodes is crucial to obtain a low classification error, compared to other approaches that reduce the number of electrodes but do not modify the filter.

This article addresses two issues in solar energy forecasting from the numerical weather prediction NWP models using machine learning. First, we are interested in determining the relevant information for the forecasting task. With this purpose, a study has been carried out to evaluate the influence on accuracy of the number of NWP grid nodes used as input for the forecasting model, as well as their relative importance. Several machine learning support vector machines and gradient boosting and feature selection algorithms linear, ReliefF, and local information analysis have been used in this study. The second aim is to be able to predict solar energy for locations where no previous production data are available. To address this goal, an approach consisting on modeling regions in the grid is proposed. Models aggregate models use as input attributes the meteorological variables relevant for the region and two new inputs to identify the location of each station: the latitude and the longitude. Those models can be used to predict energy production for existing stations and for new locations, represented by latitude and longitude. Copyright © 2015 John Wiley & Sons, Ltd.

In the context of forecasting for renewable energy, it is common to produce point forecasts but it is also important to have information about the uncertainty of the forecast. To this extent, instead of providing a single measure for the prediction, lower and upper bound for the expected value for the solar radiation are used (prediction interval). This estimation of optimal prediction intervals requires the simultaneous optimization of two objective measures: on one hand, it is important that the coverage probability of the interval is as close as possible to a given target value. On the other, in order to bound uncertainty, intervals must be narrow; this means that there is a trade-off between both objectives, as narrow intervals reduce the coverage probability for those solutions, as the actual value of solar radiation is more likely to fall outside the predicted margins. In this work we propose a multi-objective evolutionary approach that is able to optimize both goals simultaneously. The proposal uses neural networks to create complex non-linear models whose outputs are the upper and lower limits of the prediction intervals. Results have been compared with a single-objective optimization of similar neural network architectures and a baseline algorithm (quantile estimation with gradient boosting). Results show that the neural network is able to provide accurate results. Also, the multi-objective approach is able to obtain the best results and has also the advantage that a single run is able to obtain prediction intervals for any target coverage probability. This work has been funded by the Spanish Ministry of Science under contract ENE2014-56126-C2-2-R (AOPRIN-SOL project).

This work deals with wind energy prediction using meteorological variables estimated by a Numerical Weather Prediction model in a grid around the wind farm of interest. Two machine learning techniques have been tested, Support Vector Machine and Gradient Boosting Regression, in order to study their performance and compare the results. The use of meteorological variables estimated in a grid generally implies a large number of inputs to the models and the performance of models might decrease. Hence, in this context, the use of feature selection algorithms might be interesting in order to improve the generalization capability of models and/or reduce the number of attributes. We have compared three feature selection techniques based on different paradigms: Principal Components Analysis, ReliefF, and Sequential Forward Selection. Energy production data has been obtained from the Sotavento experimental wind farm. Meteorological variables have been obtained from European Centre for Medium-Range Weather Forecasts, for a 5\(\,\times \,\)5 grid around Sotavento.

Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid, September 3-5, 2014 Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis. Publicado