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The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred from the original dataset. In particular this manuscript proposes a new iterative feature construction approach based on a self-learning meta-heuristic algorithm (Harmony Search) and a solution encoding strategy (correspondingly, Cartesian Genetic Programming) suited to represent combinations of features by means of constant-length solution vectors. The proposed feature construction algorithm, coined as Adaptive Cartesian Harmony Search (ACHS), incorporates modifications that allow exploiting the estimated predictive importance of intermediate solutions and, ultimately, attaining better convergence rate in its iterative learning procedure. The performance of the proposed ACHS scheme is assessed and compared to that rendered by the state of the art in a toy example and three practical use cases from the literature. The excellent performance figures obtained in these problems shed light on the widespread applicability of the proposed scheme to supervised learning with legacy datasets composed by already refined characteristics. [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]

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.

Exposure to solar ultraviolet (UV) radiation can cause malignant keratinocyte cancer and eye disease. Developing a user-friendly, portable, real-time solar UV alert system especially or wearable electronic mobile devices can help reduce the exposure to UV as a key measure for personal and occupational management of the UV risks. This research aims to design artificial intelligence-inspired early warning tool tailored for short-term forecasting of UV index (UVI) integrating satellite-derived and ground-based predictors for Australian hotspots receiving high UV exposures. The study further improves the trustworthiness of the newly designed tool using an explainable artificial intelligence approach. An enhanced joint hybrid explainable deep neural network model (called EJH-X-DNN) is constructed involving two phases of feature selection and hyperparameter tuning using Bayesian optimization. A comprehensive assessment of EJH-X- DNN is conducted with six other competing benchmarked models. The proposed model is explained locally and globally using robust model-agnostic explainable artificial intelligence frameworks such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley additive explanations (SHAP), and permutation feature importance (PFI). The newly proposed model outperformed all benchmarked models for forecasting hourly horizons UVI , with correlation coefficients of 0.900, 0.960, 0.897, and 0.913, respectively, for Darwin, Alice Springs, Townsville, and Emerald hotspots. According to the combined local and global explainable model outcomes, the site-based results indicate that antecedent lagged memory of UVI and solar zenith angle are influential features. Predictions made by EJH-X-DNN model are strongly influenced by factors such as ozone effect, cloud conditions, and precipitation. With its superiority and skillful interpretation, the UVI prediction system reaffirms its benefits for providing real-time UV alerts to mitigate risks of skin and eye health complications, reducing healthcare costs and contributing to outdoor exposure policy. • Explainable artificial intelligence xAI for 1-hr ahead solar ultraviolet UV index prediction. • xAI model is optimized with the Bayesian feature selection algorithm. • xAI methods provide physical understandings of model prediction outcomes. • xAI model is significantly accurate for solar UV index predictions. • xAI model can help in sun exposure risk mitigation, skin and eye health protection. [ABSTRACT FROM AUTHOR]

Wind is an important source of renewable energy, often used to provide clean electricity to remote areas. For optimal extraction of this energy source, there is a need for an accurate and robust wind speed forecasting. The intermittent nature of wind makes this goal quite challenging. This research proposes a novel hybrid bidirectional LSTM (BiLSTM) model for near real-time wind speed forecasting. The hybrid model is developed using wind speed and selected climate indices from a group of neighbouring reference stations as predictors to forecast wind speed of a target station. A 3-stage feature selection is applied on the predictors to robustly extract highly significant input features. Stage 1 employs partial auto-correlation and cross-correlation, stage 2 uses the RReliefF filter algorithm, and Boruta-RF wrapper method is implemented in the final stage to improve the BiLSTM model with an efficient Bayesian optimization used for hyperparameter tuning. The proposed model has been benchmarked with comparative models including standalone and hybrid LSTM, RNN, MLP and RF. The proposed hybrid BiLSTM algorithm is found to be superior in wind speed prediction for all tested sites with ≈ 76. 6 − 84. 8 % of errors being ≤ | 0. 5 | ms − 1 . The hybrid BiLSTM model also registered the lowest Relative Root Mean Square Error (9. 6 − 23. 8 %) and Mean Absolute Percentage Error (8. 8 − 21. 5 %) among all the tested algorithms. This research ascertains that the proposed model can accurately predict wind speed and capacitate wind energy availability to be regularly monitored at a near real-time level. [Display omitted] • A novel deep learning model is proposed for near real-time wind forecasting. • BiLSTM integrated with 3-stage feature selection and Bayesian optimization is used. • Wind speed at six target sites is predicted using reference stations data. • Proposed hybrid model outperforms all comparative models. • The study has prospective implications for intermittent wind energy forecast. [ABSTRACT FROM AUTHOR]

Wind energy is an environment friendly, low-carbon, and cost-effective renewable energy source. It is, however, difficult to integrate wind energy into a mixed energy grid due to its high volatility and intermittency. For wind energy conversion systems to be reliable and efficient, accurate wind speed (WS) forecasting is fundamental. This study cascades a convolutional neural network (CNN) with a bidirectional long short-term memory (BiLSTM) in order to obtain a model for hourly WS forecasting by utilizing several meteorological variables as model inputs to study their effects on predicted WS. For input selection, the mutation grey wolf optimizer (TMGWO) is used. For efficient optimization of CBiLSTM hyperparameters, a hybrid Bayesian Optimization and HyperBand (BOHB) algorithm is used. The combined usage of TMGWO, BOHB, and CBiLSTM leads to a three-phase hybrid model (i.e., 3P-CBiLSTM). The performance of 3P-CBiLSTM is benchmarked against the standalone and hybrid BiLSTMs, LSTMs, gradient boosting (GBRs), random forest (RFRs), and decision tree regressors (DTRs). The statistical analysis of forecasted WS reveals that the 3P-CBiLSTM is highly effective over the other benchmark forecasting methods. This objective model also registers the highest percentage of forecasted errors (≈ 53.4 – 81.8%) within the smallest error range ≤ | 0. 25 | ms − 1 amongst all tested study sites. Despite the remarkable results achieved, the CBiLSTM model cannot be generally understood, so the eXplainable Artificial Intelligence (xAI) technique was used for explaining local and global model outputs, based on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). Both of the xAI methods determined that the antecedent WS is the most significant predictor of the short-term WS forecasting. Therefore, we aver that the proposed model can be employed to help wind farm operators in making quality decisions in maximizing wind power integration into the grid with reduced intermittency. • Explainable hybrid deep learning model proposed for hourly wind speed forecasting. • Grey wolf optimizer with a two-phase mutation designed for feature selection. • Bayesian Optimization and HyperBand used for efficient hyperparameter tuning. • Local Interpretable Model-Agnostic Explanations offer local model interpretation. • SHapley Additive exPlanations provide global model interpretation. [Display omitted] [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]

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]

Global solar radiation (GSR) prediction plays an essential role in planning, controlling and monitoring solar power systems. However, its stochastic behaviour is a significant challenge in achieving satisfactory prediction results. This study aims to design an innovative hybrid prediction model that integrates a feature selection mechanism using a S lime-Mould algorithm, a C onvolutional-Neural-Network (CNN), a L ong–Short-Term-Memory Neural Network (LSTM) and a final CNN with Multilayer-Perceptron output (SCLC algorithm hereafter). The proposed model was applied to six solar farms in Queensland (Australia) at daily temporal horizons in six different time steps. The comprehensive benchmarking of the obtained results with those from two Deep-Learning (CNN-LSTM, Deep-Neural-Network) and three Machine-Learning (Artificial-Neural-Network, Random-Forest, Self-Adaptive Differential-Evolutionary Extreme-Learning-Machines) models highlighted a higher performance of the proposed prediction model in all the six selected solar farms. From the results obtained, this work establishes that the designed SCLC algorithm could have a practical utility for applications in renewable and sustainable energy resource management. [Display omitted] • A novel deep learning model for Global Solar Radiation prediction is proposed. • The model integrates deep networks with Slime Mould Algorithm for feature selection. • Meteorological data at solar farms in Australia are considered as variables. • Error analysis and statistical metrics show the model's practicality. [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]

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, the CNN algorithm is used to extract local patterns as well as common features that occur recurrently in time series data at different intervals. Then, the SVR is subsequently adopted to replace the fully connected CNN layers to predict the daily GSR time series data at six solar farms in Queensland, Australia. To develop the hybrid CSVR model, we adopt the most pertinent meteorological variables from Global Climate Model and Scientific Information for Landowners database. From a pool of Global Climate Models variables and ground-based observations, the optimal features are selected through a metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters of the proposed CSVR are optimized by mean of the HyperOpt method, and the overall performance of the objective algorithm is benchmarked against eight alternative DL methods, and some of the other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML and M5TREE) methods. The results obtained shows that the proposed CSVR model can offer several predictive advantages over the alternative DL models, as well as the conventional ML models. Specifically, we note that the CSVR model recorded a root mean square error/mean absolute error ranging between ≈ 2.172–3.305 MJ m2/1.624–2.370 MJ m2 over the six tested solar farms compared to ≈ 2.514–3.879 MJ m2/1.939–2.866 MJ m2 from alternative ML and DL algorithms. Consistent with this predicted error, the correlation between the measured and the predicted GSR , including the Willmott's, Nash-Sutcliffe's coefficient and Legates & McCabe's Index was relatively higher for the proposed CSVR model compared to other DL and Machine Learning methods for all of the study sites. Accordingly, this study advocates the merits of CSVR model to provide a viable alternative to accurately predict GSR for renewable energy exploitation, energy demand or other forecasting-based applications. [Display omitted] • This paper presents a hybrid CSVR algorithm for global solar radiation prediction. • The proposed approach combines Convolutional Neural Network with a Support Vector Machine. • An Atom Search-based feature selection is applied to improve the performance of the system. • Experiments are carried out in data at six solar energy farms in Queensland, Australia. • The results show the efficacy of deep learning CSVR for solar energy prediction problems. [ABSTRACT FROM AUTHOR]