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Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. [ABSTRACT FROM AUTHOR]

Prediction problems in timed datasets related to human activities are especially difficult to solve, because of the specific characteristics and the scarce number of predictive (input) variables available to tackle these problems. In this paper, we try to find out whether Machine Learning (ML) approaches can be successfully applied to these problems. We deal with timed datasets with human activity patterns, in which the input variables are exclusively related to the day or type of day when the prediction is carried out and, usually, to the meteorology of those days. These problems with a marked human activity pattern frequently appear in mobility and traffic-related problems, delivery prediction (packets, food), and many other activities, usually in cities. We evaluate the performance in these problems of different ML methods such as artificial neural networks (multi-layer perceptrons, extreme learning machines) and support vector regression algorithms, together with an Analogue-type (KNN) approach, which serves as a baseline algorithm and provides information about when it is expected that ML approaches will fail, by looking for similar situations in the past. The considered ML algorithms are evaluated in four real prediction problems with human activity patterns, such as school absences, bike-sharing demand, parking occupation, and packets delivered in a post office. The results obtained show the good performance of the ML algorithms, revealing that they can deal with scarce information in all the problems considered. The results obtained have also revealed the importance of including meteorology as the input variables, showing that meteorology is frequently behind demand peaks or valleys in this kind of problem. Finally, we show that having a number of similar situations in the past (training set) prevents ML algorithms from making important mistakes in the prediction obtained. [ABSTRACT FROM AUTHOR]

Wind Power Ramp Events (WPREs) are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML) regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines) and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains). Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem. [ABSTRACT FROM AUTHOR]

Some materials, such as reinforced concrete (RC), introduce non-linear constitutive relationships into structural models. The characterization of their structural load–strain response is usually approached by using iterative strategies, such as Newton-type methods. However, these iterative strategies may not predict the non-linear structural response of RC elements (beams) due to convergence problems (non-consistent elements). In this work we propose a novel solution to overcome the convergence failures of RC structural elements subjected to shear force modeled by Compression Field Theories (CFTs). First, a Coral Reef Optimization with Substrate Layers (CRO-SL) metaheuristic is used to solve a fitting problem between numerical and experimental results on a set of real beams tested in shear, for which the system is solvable. Then, the outputs of the CRO-SL, plus the mechanical and geometric parameters of the RC beams, are used to train different Machine Learning (ML) regression methods. These regressors are then able to predict the tensile strains of the non-consistent RC elements. The characterization of such strains allows modifying the parameterization of the system of equations, overcoming thus the convergence problem of Newton-type methods in these cases. Experiments over a set of 72 real RC beams have shown the good performance of our approach. In them we have carried out a comparison among different ML regressors including Multi-Layer perceptrons, Support Vector Regression and Random Forest, plus feature reduction techniques such as PCA. [Display omitted] • Hybrid Machine Learning — Metaheuristics are proposed for improving convergence in mechanical models. • The combination of Coral Reefs Optimization algorithm with different regression models shows high performance. • Compression Field Theories are considered to predict the shear response of reinforced concrete beams. • Principal Component Analysis allows improving the shear strength experimental adjustment. [ABSTRACT FROM AUTHOR]

Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field. [ABSTRACT FROM AUTHOR]

We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors. [ABSTRACT FROM AUTHOR]

The internationalization of a company is widely understood as the corporative strategy for growing through external markets. It usually embodies a hard process, which affects diverse activities of the value chain and impacts on the organizational structure of the company. There is not a general model for a successful international company, so the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy and market characteristics of the company at hand. This paper presents a novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data. Specifically, we propose a hybrid algorithm composed by a grouping-based harmony search (HS) approach and an extreme learning machine (ELM) ensemble. The proposed hybrid scheme further incorporates a feature selection method, which is obtained by means of a given group in the HS encoding format, whereas the ELM ensemble renders the final accuracy metric of the model. Practical results for the proposed hybrid technique are obtained in a real application based on the exporting success of Spanish manufacturing companies, which are shown to be satisfactory in comparison with alternative state-of-the-art techniques. [ABSTRACT FROM PUBLISHER]

Abstract: Hyper-parameters estimation in regression Support Vector Machines (SVMr) is one of the main problems in the application of this type of algorithms to learning problems. This is a hot topic in which very recent approaches have shown very good results in different applications in fields such as bio-medicine, manufacturing, control, etc. Different evolutionary approaches have been tested to be hybridized with SVMr, though the most used are evolutionary approaches for continuous problems, such as evolutionary strategies or particle swarm optimization algorithms. In this paper we discuss the application of two different evolutionary computation techniques to tackle the hyper-parameters estimation problem in SVMrs. Specifically we test an Evolutionary Programming algorithm (EP) and a Particle Swarm Optimization approach (PSO). We focus the paper on the discussion of the application of the complete evolutionary-SVMr algorithm to a real problem of wind speed prediction in wind turbines of a Spanish wind farm. [ABSTRACT FROM AUTHOR]

This article focuses on a twofold approach to improve the performance of genetic algorithms (GAs) in the feature selection problem (FSP). The feature selection problem (FSP) is an open issue in machine learning, which basically consists of finding a subset of input features that describes the underlying system structure as well or better than all available features. There is a more general problem, known as feature extraction problem, in which one has to construct a few features that encode all of the information contained in the available original ones; the FSP is, in fact a particularization of this problem.

This work presents a full statistical analysis and accurate prediction of low-visibility events due to fog, at the A-8 motor-road in Mondoñedo (Galicia, Spain). The present analysis covers two years of study, considering visibility time series and exogenous variables collected in the zone affected the most by extreme low-visibility events. This paper has then a two-fold objective: first, we carry out a statistical analysis for estimating the fittest probability distributions to the fog event duration, using the Maximum Likelihood method and an alternative method known as the L-moments method. This statistical study allows association of the low-visibility depth with the event duration, showing a clear relationship, which can be modeled with distributions for extremes such as Generalized Extreme Value and Generalized Pareto distributions. Second, we apply a neural network approach, trained by means of the ELM (Extreme Learning Machine) algorithm, to predict the occurrence of low-visibility events due to fog, from atmospheric predictive variables. This study provides a full characterization of fog events at this motor-road, in which orographic fog is predominant, causing important traffic problems during all year. We also show how the ELM approach is able to obtain highly accurate low-visibility events predictions, with a Pearson correlation coefficient of 0.8 , within a half-hour time horizon, enough to initialize some protocols aiming at reducing the impact of these extreme events in the traffic of the A-8 motor road. [ABSTRACT FROM AUTHOR]

This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem. [ABSTRACT FROM AUTHOR]

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario. [ABSTRACT FROM AUTHOR]

Prediction of Total Cloud Cover (TCDC) from numerical weather simulation models, such as Global Forecast System (GFS), can aid renewable energy engineers in monitoring and forecasting solar photovoltaic power generation. A major challenge is the systematic bias in TCDC simulations induced by the errors in the numerical model parameterization stages. Correction of GFS-derived cloud forecasts at multiple time steps can improve energy forecasts in electricity grids to bring better grid stability or certainty in the supply of solar energy. We propose a new kernel ridge regression (KRR) model to reduce bias in TCDC simulations for medium-term prediction at the inter-daily, e.g., 2–8 day-ahead predicted TCDC values. The proposed KRR model is evaluated against multivariate recursive nesting bias correction (MRNBC), a conventional approach and eight machine learning (ML) methods. In terms of the mean absolute error (MAE), the proposed KRR model outperforms MRNBC and ML models at 2–8 day ahead forecasts, with MAE ≈ 20–27%. A notable reduction in the simulated cloud cover mean bias error of 20–50% is achieved against the MRNBC and reference accuracy values generated using proxy-observed and non-corrected GFS-predicted TCDC in the model's testing phase. The study ascertains that the proposed KRR model can be explored further to operationalize its capabilities, reduce uncertainties in weather simulation models, and its possible consideration for practical use in improving solar monitoring and forecasting systems that utilize cloud cover simulations from numerical weather predictions. [Display omitted] • KRR model for bias correction of Total Cloud Cover forecasts is proposed. • KRR reduces bias in two-to-eight-day ahead forecasts at 0, 3, 6 UTC. • Cloud cover forecast error is reduced between 20-50% against conventional error reduction methods. • KRR is validated with multivariate recursive nested bias correction and other models. • KRR reduces bias in Total Cloud Cover forecasts for solar energy forecast systems. [ABSTRACT FROM AUTHOR]

Global Solar Radiation (GSR) prediction models are critical to improve the dispatch, control, and stabilization of solar renewable power, and to integrate the solar energy into the electrical grid system. GSR, especially on a short-term scale, can have important fluctuations, which may affect the total energy expected to be supplied to the grid. To overcome this issue, prediction models with a high forecasting performance are needed. In this paper a novel framework based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Deep Residual Network with Bidirectional long short-term memory, i.e., DRESNET model, is proposed for obtaining accurate multi-step ahead GSR predictions. To train the proposed ICEEMDAN-DRESNET hybrid model, minute-level daylight data from Energy Sector Management Assistance Program in Nepalgunj (mid-western Nepal) are used. The results demonstrate ICEEMDAN-DRESNET model is an excellent tool for short-term solar energy monitoring, yielding excellent predictions, in all metrics such as MAE 9.769 W/m2, MAPE 5.657 % , TIC 1.143, CPI 4.739, and TIC 0.023 for 5-min time-horizon predictions, improving the results from the benchmark models. As the forecasting time-horizon is increased, the ICEEMDAN-DRESNET model accuracy drops, with MAE 33.672 W/m2; MAPE 31.749 % for 1-hr, MAE 22.625 W/m2; MAPE 18.312 % (30-min) and MAE 14.897 W/m2; MAPE 10.358 % (15-min), also better than the benchmark models. The results confirm the competitive merit of ICEEMDAN and DRESNET integration to improve deep learning and the potential of proposed model for the monitoring of solar or other renewable (e.g., wind or solar) energies. [Display omitted] • A deep learning algorithm for short-term solar prediction is proposed. • The system considers deep residual and bidirectional long short-term memory network models. • Nepal (a solar-rich region) is selected to evaluate the deep learning algorithm. • Tests are carried out at multi-step ahead horizons of 5-min, 10-min, 30-min and 1-h. • Excellent performance of the proposed deep learning system is reported. [ABSTRACT FROM AUTHOR]

Implementing key engineering solutions to optimise the operation of energy industries requires daily electricity demand forecasting and including uncertainty, to promote markets insight analysis as part of their strategic planning, regulating and supplying electricity to consumers. This paper proposes hybrid artificial intelligence models combining convolutional neural networks (CNN) as a feature extraction algorithm with extreme learning machines (ELM) as a framework to predict electricity demand with confidence intervals generated by Kernel Density Estimation (KDE) approaches. In order to develop CELM-KDE model, time-lagged series of daily electricity demand with local climate variables based on the air temperature, atmospheric vapour pressure, evaporation, solar radiation, humidity and sea level pressures are used to train the proposed CELM-KDE hybrid model. In order to fully evaluate the newly developed model from a point-based, as well as a probabilistic prediction strategy, the observed and predicted electricity demand as well as the probability distribution of errors are analysed using KDE method that operates without prior data distribution assumptions. Based on observed and predicted electricity demand and the relevant probabilistic confidence intervals generated by the CELM-KDE model, the final results show that the proposed method attains significantly better probability interval predictions than traditionally-used point-based models. The proposed CELM-KDE model is demonstrated to be highly effective in providing a comprehensive coverage of predicted errors, as well as providing greater insights into the average bandwidth and detailed predicted electricity demand in the testing stage. The results also indicate that the proposed hybrid model is a reliable decision support tool to develop engineering solutions in area of energy modelling, monitoring and forecasting, which could potentially be useful to the industry policymakers. We show that the point-and probabilistic-based electricity demand predictive models can be employed as an effective tool to improve accuracy of forecasting and provision of insights for national electricity markets and key energy industry stakeholder application tools. [Display omitted] • A hybrid deep learning model for daily electricity demand prediction is proposed. • A Kernel Density Estimation is applied to derive probabilistic prediction intervals. • The algorithm quantifies uncertainties in point-based electricity demand predictions. • The proposed model outperforms deep learning models using statistical uncertainty indicators. [ABSTRACT FROM AUTHOR]

In this paper the performance of different Machine Learning and Deep Learning approaches is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of bike sharing demand in Madrid and Barcelona (Spain) is approached, for different prediction time-horizons, and also a problem of cable car demand forecasting in Madrid city. An important number of predictive variables are considered, which are grouped into four different sets (categorical/calendrical, persistence-based, meteorological and, as a novelty of the paper, information about analogue past instances), whose relevance is studied for all cases. A feature selection mechanism is also incorporated in order to improve the prediction accuracy of the proposed algorithms. A total of 12 different multivariate regression techniques are implemented, covering from Machine Learning methods to time-series Deep Learning approaches. Excellent results in all the prediction problems approached are reported. Finally, the consequences of obtaining accurate prediction in these three problem of green mobility in big cities are discussed. In addition, it is studied how the results could be exported to other similar cases in more general urban mobility studies. Novelties of the work include: (1) Addressing the forecast problem of passenger flow on a cable car using ML and DL multivariate techniques; (2) using the demand of analogous past instances as an additional feature to solve the demand prediction problems; and (3) the extraction of global conclusions about feature relevance when addressing a demand forecasting problem in green mobility. [ABSTRACT FROM AUTHOR]

In the context of reinforced concrete members subjected to shear, the steel behaviour, assumed as embedded in the concrete, has been modelled through different strategies. One of them, the so-called Refined Compression Field Theory (RCFT), is based on the concept of the concrete tension stiffening area, and shows a better fitting with the experimental results than other shear theories. However, for certain standard design conditions, the RCFT non-linear formulation does not throw a real physical solution for the steel yield strain, what hinders its numerical calculation. In fact, previous works have defined a solvability region for such strain. This non-linear equation is usually solved by iterative methods. However, the convergence of these methods depends on the root location. Moreover, their robustness may diminish in the boundary of the solvability region. This work validates Machine Learning (ML) approaches as alternative tools for the iterative prediction of the steel yield strain. To this aim, the calculation of the steel yield strain is performed in two stages, first locating the root to calculate using ML-based classification techniques, and second calculating such root with an accurate adjustment as a function of its location, using ML-based regression algorithms. As result, the efficiency of the proposed method no longer depends on the steel strain location. Finally, despite the high influence of the concrete bond parameter in the yield strain field, the ML prediction error is homogeneous over all the problem physical domain. [Display omitted] • Accurate calculation of steel yield strain in reinforced concrete is tackled. • Iterative numerical methods usually present convergence problems. • Machine Learning algorithms may improve the calculation of steel yield strain. • Decision Trees, Random Forest and Support Vector Machines are applied. [ABSTRACT FROM AUTHOR]

Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem. [Display omitted] • A Hybrid deep learning model is proposed for daily electricity demand prediction. • Convolutional Neural Networks and Echo State Networks are integrated into a single forecasting algorithm. • The proposed deep learning-based approach is excellent in electricity demand prediction. • The proposed algorithm has practical use in inter-daily electricity demand prediction. [ABSTRACT FROM AUTHOR]

In this research, a machine learning model namely extreme learning machine (ELM) is proposed to predict the compressive strength of foamed concrete. The potential of the ELM model is validated in comparison with multivariate adaptive regression spline (MARS), M5 Tree models and support vector regression (SVR). The Lightweight foamed concrete is produced via creating a cellular structure in a cementitious matrix during the mixing process, and is widely used in heat insulation, sound attenuation, roofing, tunneling and geotechnical applications. Achieving product consistency and accurate predictability of its performance is key to the success of this technology. In the present study, an experimental database encompassing pertinent data retrieved from several previous studies has been created and utilized to train and validate the ELM, MARS, M5 Tree and SVR machine learning models. The input parameters for the predictive models include the cement content, oven dry density, water-to-binder ratio and foamed volume. The predictive accuracy of the four models has been assessed via several statistical score indicators. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy, improving MARS, M5 Tree and SVR models. Hence, the ELM model could be employed as a reliable and accurate data intelligent approach for predicting the compressive strength of foamed concrete, saving laborious trial batches required to attain the desired product quality. [ABSTRACT FROM AUTHOR]

Authorship attribution, conceived as the identification of the origin of a text between different authors, has been a very active area of research in the scientific community mainly supported by advances in Natural Language Processing (NLP), machine learning and Computational Intelligence. This paradigm has been mostly addressed from a literary perspective, aiming at identifying the stylometric features and writeprints which unequivocally typify the writer patterns and allow their unique identification. On the other hand, the upsurge of social networking platforms and interactive messaging have undoubtedly made the anonymous expression of feelings, the sharing of experiences and social relationships much easier than in other traditional communication media. Unfortunately, the popularity of such communities and the virtual identification of their users deploy a rich substrate for cybercrimes against unsuspecting victims and other forms of illegal uses of social networks that call for the activity tracing of accounts. In the context of one-to-one communications this manuscript postulates the identification of the sender of a message as a useful approach to detect impersonation attacks in interactive communication scenarios. In particular this work proposes to select linguistic features extracted from messages via NLP techniques by means of a novel feature selection algorithm based on the dissociation between essential traits of the sender and receiver influences. The performance and computational efficiency of different supervised learning models when incorporating the proposed feature selection method is shown to be promising with real SMS data in terms of identification accuracy, and paves the way towards future research lines focused on applying the concept of language typicality in the discourse analysis field. [ABSTRACT FROM AUTHOR]

Over the last decades, different machine learning techniques have been used to detect climate change patterns, mostly using data from measuring stations located in different parts of the world. Some previous studies focus on temperature as primary variable of study, though there have been other works focused on precipitation or even wind speed as objective variable. In this paper, we use the self-organized Second Order Data Coupled Clustering (SODCC) algorithm to carry out a spatio-temporal analysis of temperature patterns in Europe. By applying the SODCC we identify three different regimes of spatio-temporal correlations based on their geographical extent: small, medium, and large-scale regimes. Based on these regimes, it is possible to detect a change in the spatio-temporal trend of air temperature, reflecting a shift in the extent of the correlations in stations in the Iberian Peninsula and Southern France. We also identify an oscillating spatio-temporal trend in the Western Asia region and a stable medium-scale regime affecting the British Isles. These results are found to be consistent with previous studies in climate change. The patterns obtained with the SODCC algorithm may represent a signal of climate change to be taken into account, and so the SODCC could be used as detection method. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid approach for Global Solar Radiation (GSR) prediction problems, based on deep learning approaches. Predictive models are useful ploys in solar energy industries to optimize the performance of photovoltaic power systems. Specifically, in this work we develop a new 4-phase hybrid CXGBRFR framework, which includes a deep learning Convolutional Neural Network, an Extreme Gradient Boosting with Random Forest Regression, and a Harris Hawks Optimization for initial feature selection. The proposed system has been tested in a problem of daily GSR prediction at six solar farms in Australia. Data from three global climate models (GCM) (CSIRO-BOM ACCESS1-0, MOHC Hadley-GEM2-CC and MRI MRI-CGCM3) have been considered as predictive (input) variables for the proposed approach. The variables from these GCMs contain enough information to obtain an accurate prediction of the GSR at each solar farm. The performance of the proposed approach is compared against different deep and shallows learning approaches: Deep Belief Network, Deep Neural Network, Artificial Neural Network, Extreme Learning Machine and Multivariate Auto-Regressive Spline models. We show that the proposed approach exhibits an excellent performance in GSR prediction, against all alternative approaches in all solar farms considered. [Display omitted] • We develop hybrid deep learning model to predict solar radiation • We optimize the algorithm with Harris Hawks, gradient boosting and random forest • We adopt global climate model and observed predictors as learning model inputs • We build an expert system with relevant knowledge in context of solar application • Our methods benefit solar photovoltaic power monitoring in solar engineering problems. [ABSTRACT FROM AUTHOR]