Abstract Effective control of Energy Storage Systems (ESS) is crucial for the secure and profitable operation of microgrids. In this context, ESSs are essential for enhancing the overall grid resilience, balancing supply, and mitigating voltage and frequency variations. This paper presents a novel neuroevolutionary method, coupling a modified version of the Multi-Objective Evolutionary Policy Search (MEPS) algorithm with the Cross-Entropy method, aimed at optimizing an ESS control problem. The modified MEPS, named Cascade-MEPS, employs a cascade weights mutation operator to refine policies by focusing on the most recent hidden node, ensuring localized and non-disruptive adjustments. The resulting algorithm, referred to as cross-entropy Cascade-MEPS (CE-CMEPS), utilizes the cross-entropy method as a depth initialization strategy, conducting an initial exploration of the weights space to initialize the population prior to Cascade-MEPS execution. Experimental validation on a newly proposed multi-objective ESS control problem demonstrates the efficacy of CE-CMEPS, showcasing performance improvements and reduced variation compared to standalone MEPS. Our results show that CE-CMEPS is an effective ESS discharge controller and a sustainable multi-objective reinforcement learning solution.

The surveillance multi-sensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multi-sensor placement for indoor surveillance. Our approach is focused on security surveillance of sensible indoor spaces, such as military installations, where distinct security levels can be considered. We propose an evolutionary algorithm to solve the problem, in which a novel special encoding (integer encoding with binary conversion) and effective initialization have been defined to improve the performance and convergence of the proposed algorithm. We also consider the probability of detection for each surveillance point, which depends on the distance to the sensor at hand, to better model real-life scenarios. We have tested the proposed evolutionary approach in different instances of the problem, varying both size and difficulty and obtained excellent results regarding the cost of sensors’ placement and convergence time of the algorithm. • Tackle a spatially distributed multi-sensors placement problem with security levels. • Useful for security surveillance of sensible indoor spaces, such as military installations. • Proposal of an evolutionary algorithm with specific encoding and effective initialization. • Comparison with alternative algorithms in different-sized scenarios.

In this paper we propose and discuss different Deep Learning-based ensemble algorithms for a problem of low-visibility events prediction due to fog. Specifically, seven different Deep Learning (DL) architectures have been considered, from which multiple individual learners are generated. Hyperparameters of the models, including parameters concerning data preprocessing, models architecture and training procedure, are randomly selected for each model within a pre-defined discrete range. Also, every model is trained with slightly different data sampled randomly, assuring that every models introduce variety in the ensemble. Then, three different information fusion techniques are employed to build the ensemble models. The influence of the filtering process and the elitism level (the percentage of the individual models entering the ensemble) is also assessed. The performance of the proposed methodology have been tested in two real problems of low-visibility events prediction due to orographical and radiation fog, at the north of Spain. Comparison with different Machine Learning, alternative DL algorithms and meteorological-based methods show the good performance of the proposed deep learning ensembles in this problem.

Microgrids have become popular candidates for integrating diverse energy sources into the power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is a type of Unit Commitment problem, where a player operates a microgrid with diverse renewable generators integrated with an external supplier. Calculating the economic dispatch of each committed unit on a planning horizon is an NP-hard problem, and therefore, finding an exact solution is difficult. This paper presents a multi-objective solution to the ERM problem from the perspective of battery operation and external supplier dispatch. First, a novel multi-objective decision problem modeling is proposed that considers three objectives: cost, greenhouse gas emissions, and battery degradation. This framework involves a learning agent that controls the depth of discharge of a Lithium-Ion battery. To address the proposed problem, a new multi-objective algorithm called Multi-Objective Evolutionary Policy Search (MEPS) is introduced. The proposed algorithm uses NeuroEvolution of Augmenting Topologies structure to evolve artificial neural networks for estimating action-preference values considering multi-objective rewards. The MEPS performance is evaluated on both standard and newly-proposed benchmark problems, using the hypervolume as the evaluation metric. When compared to standard deep reinforcement learning, results showed that MEPS provides cost-effective, environmentally friendly, and efficient energy storage management solutions. Furthermore, MEPS effectively solves the proposed ERM problem by finding neural networks with a small number of nodes and connections, which are suitable for use in embedded control systems. Overall, MEPS proved to be a promising multi-objective approach in the transition to clean energy resources.

Abstract Optimal power flow (OPF) is a complex, highly nonlinear, NP-hard optimization problem, in which the goal is to determine the optimal operational parameters of a power-related system (in many cases a type of smart or micro grid) which guarantee an economic and effective power dispatch. In recent years, a number of approaches based on metaheuristics algorithms have been proposed to solve OPF problems. In this paper, we propose the use of the Cross-Entropy (CE) method as a first step depth search operator to assist population-based evolutionary methods in the framework of an OPF problem. Specifically, a new variant of the Coral Reefs Optimization with Substrate Layers algorithm boosted with CE method (CE+CRO-SL) is presented in this work. We have adopted the IEEE 57-Bus System as a test scenario which, by default, has seven thermal generators for power production for the grid. We have modified this system by replacing three thermal generators with renewable source generators, in order to consider a smart grid approach with renewable energy production. The performance of CE+CRO-SL in this particular case study scenario has been compared with that of well-known techniques such as population’s methods CMA-ES and EPSO (both boosted with CE). The results obtained indicate that CE+CRO-SL showed a superior performance than the alternative techniques in terms of efficiency and accuracy. This is justified by its greater exploration capacity, since it has internally operations coming from different heuristics, thus surpassing the performance of classic methods. Moreover, in a projection analysis, the CE+CRO-SL provides a profit of millions of dollars per month in all cases tested considering the modified version of the IEEE 57-Bus smart grid system.

In this article, we propose a novel flexible architecture, with different algorithmic procedures, for effective story segmentation of broadcast news from subtitle files. The proposed system exploits spatial and temporal distance, as well as sentence similarity, to classify different stories in news broadcasts. The computational algorithms which form the architecture mainly focus on each sentence's features (temporal distance, spatial distance, and semantic similarity), and are combined to build an overall classifier. The first algorithm in the architecture focuses on the segmentation task, detecting boundaries between news. The second and third algorithms identify high semantic correlation between pieces of text, whether they are consecutive in space or not. Video Text Track (VTT) subtitle files are used to evaluate the performance of the proposed approach, although any file format that includes temporal information could also be considered. These VTT files may contain text errors and inaccuracies, and the proposed algorithms have been designed to deal with noisy content.

Optimal active–reactive power dispatch problems (OARPD) are considered large scale optimization problems with a high nonlinear complexity. Usually, in OARPD the objective is to minimize the cost of the system operation. In 2018, the IEEE PES committee proposed a competition, the “Operational planning of sustainable power systems”, in which a test bed relating the OARPD and a renewable energy generation challenge within a smart grid was proposed. In this work we consider three test scenarios proposed in that competition. Specifically, we present a hybrid meta-heuristic optimization approach applied to the OARPD, the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO), to tackle these test scenarios. Comparative results with other algorithms such as CMA-ES, EPSO, and CEEPSO indicate that C-DEEPSO shows a competitive performance when solving the OARPD problems.

In this work we present a pattern classification approach coupling the Neighbourhood Component Analysis (NCA) classifier with the Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO). The standard NCA uses the conjugate gradient method to minimize the classification error. Here we propose an approach using the C-DEEPSO instead. In the experimental design, the coupled approach is applied to 20 benchmark data sets, and its performance is compared with the standard NCA using the conjugate gradient. The experimental analysis shows the usage of an evolutionary approach to enhance the performance of a machine learning algorithm can be competitive when compared to well-known iterative optimization techniques, and even outperform them in some problems. A real-world problem classifying cyber-attacks to an industrial control system of gas pipelines is also solved by the proposed approach. The results obtained indicate the proposed approach can successfully identify possible cyber-attacks to the control system. In this way, the NCA coupled to C-DEEPSO can work as an Intrusion Detection Systems (IDS), being able to guarantee an acceptable security level.

The unit dispatch problem is defined as the attribution of operational values to each generation unit inside a hydro-power plant (HPP), given some criteria such as the total power to be generated, or the operational bounds of each unit. An optimal dispatch programming for hydroelectric units in HPP provides a larger production of electricity, with minimal water use. This paper presents an evolutionary approach to optimize the multi-criteria electric dispatch problem in a general HPP, based on a Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm. The proposed approach integrates mathematical models and evolutionary swarm computation. The experimental analysis shows that the proposed MESH algorithm is able to reach competitive results when compared with classical evolutionary algorithms, the NGA-II and SPEA2 basing on ANOVA inference test. Results also show that the proposed MESH is able to save a large amount of water in the energy production process, supplying the requested load, and minimizing blackout risks and generating a profit around $275,000 monthly.

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.

This paper discuses the performance of two different Evolutionary Algorithms (EAs) in a problem of Optimal Placement of Distributed Power Generation (OPDPG) in Micro-Grids (MGs). Specifically, the problem consists of choosing the node/nodes to locate a number of different distributed generators with different technologies (such as micro wind turbines, photovoltaic panels, etc.), in such a way that the electrical power losses along a given time period (T) in the MG are minimized. We consider a situation where the network topology is already defined and where each node can have a load with different profiles allocated. The consumption profiles are real measurements of different types (residential, industrial, etc.) and will be hourly evaluated. The generations profiles are also real measurement data from different generation technologies. We consider two different encodings the EAs: first a binary-encoding approach, where each wind generator is represented by 2 bits and each solar generator by N bits, where N is the number of nodes that form the MG; and second, an integer-encoding approach, where both wind and PV generators are represented by 1 and 4 integer elements, respectively. Experiments are performed by considering three different MG topologies, with different number of nodes, in order to test the behavior of the algorithms with search spaces of increasing size. In these experimental scenarios we show how the binary approach attains better solutions than the integer-encoding approach, tough the computational time of the former is higher.

In this paper we propose an approach for feature selection in a problem of significant wave height prediction, to improve the exploitation of marine energy. The method that we present, a Grouping Genetic Algorithm — Extreme Learning Machine approach (GGA-ELM), mainly tries to improve the prediction performance of the regressors, providing more effective predictors and good performance in the final significant wave height prediction. In this method, the GGA looks for several subsets of features, and the ELM provides the fitness of the algorithm, through its accuracy on significant wave height prediction. The GGA is able to evolve different groups of features in parallel, which may improve the performance of the prediction obtained. After the feature selection process with the GGA-ELM, the final results are obtained by applying an ELM and also by a Support Vector Regressor algorithm, both working on the best GGA groups of features previously evolved. In the experimental part of the paper, we show the performance of the proposed approach in a real problem of significant wave height prediction at the West Coast of the USA, using variables directly obtained from several measuring buoys.

During the past years solar radiation prediction has become increasingly relevant among the scientific community and Machine Learning techniques have proven to be a useful tool to automatically learn an accurate prediction model. In this paper, we move one step further and try to gain interpretability during the learning process by introducing a novel feature selection approach. Our method trains a set of bootstrapped SVR classifiers to detect those features that are informative for the prediction task. This way we obtain a more robust set of selected features compared to other selection methods. This allows us to detect in a multivariate fashion not only the features needed to solve the prediction task, but also those that are informative for the problem at hand. The application of this algorithm to a Weather Research and Forecasting model, and its comparison to some state of the art tools, shows the advantages of the proposed method both in terms of resistance to overfitting, selection consistency and interpretability, while at the same time improving performance in terms of prediction accuracy.

This paper proposes to tackle the structure design and operation of a Micro-Grid in a jointly way, by means of a novel nested Evolutionary Algorithms (EAs) approach. Specifically, in an scenario of variable electricity prices in an hourly basis, we apply different EAs, nested, to obtain optimal values for the sizing of generators and Energy Storage System (ESS), also to obtain the optimal values for each access tariff periods (structure part of the MG), and ESS scheduling (operational part of the MG). The proposed nested EAs starts from an initial solution for the ESS scheduling given by a deterministic approach (DA algorithm), from which an initial structure part is obtained by means of a first evolution. This part is set, and a different EA is then applied to obtain an improved ESS scheduling, which will be set to apply a different EA for the structure part. This scheme is applied in a sequential fashion for a number of evolutions. We will show that the proposed evolution scheme is able to obtain excellent results in terms of MG design, better than those by a single EA with the same number of function evaluations.

This paper tackles the problem of sizing a standalone hybrid photovoltaic-batteries-hydrogen (PV-hydrogen) system, by applying an evolutionary algorithm. The system is specifically designed to cover the power necessities of remote, isolated telecommunications facilities, so it must be able to work in an unattended way during at least 2 years. Under this specific constraint, we develop an evolutionary algorithm which optimizes the number of PV panels and their distribution to feed two different arrays of batteries, and also the slope and azimuth of the panels. The well-known simulation program TRNSYS has been used in order to simulate the behavior of the real PV-hydrogen system. The evolutionary algorithm looks for the set of parameters which best performance of the system provide, in terms of hydrogen pressure remaining after two years and cost of the complete PV panels. The performance of the proposed evolutionary algorithm has been tested for the case of a real PV-hydrogen system sited at National Spanish Institute for Aerospace Technology (INTA), Torrejón de Ardoz, Madrid, Spain, where the proposed approach obtained a good solution which fulfils the constraint specifications of the system.

A proof-of-concept design of a patient monitoring solution for intensive care unit environments has been presented. It is end-to-end standard-based, using ISO/IEEE 11073 (X73) in the bedside environment and EN13606 to communicate the information to an electronic healthcare record (EHR) server. At the bedside end, the system is a plug-and-play sensor network communicating with a gateway that collects medical information and sends the data to a monitoring server. The monitoring server transforms this information into an EN13606 extract to be stored on the EHR server. The system has been implemented to comply with the last X73 and EN13606 available versions and tested in a laboratory environment to demonstrate the feasibility of an end-to-end standard-based solution.

Information and telecommunication technologies are called to play a major role in the changes that healthcare systems have to face to cope with chronic disease. This paper reports a telemedicine experience for the home care of chronic patients suffering from chronic obstructive pulmonary disease (COPD) and an integrated system designed to carry out this experience. To determine the impact on health, the chronic care telemedicine system was used during one year (2002) with 157 COPD patients in a clinical experiment; endpoints were readmissions and mortality. Patients in the intervention group were followed up at their homes and could contact the care team at any time through the call center. The care team shared a unique electronic chronic patient record (ECPR) accessible through the web-based patient management module or the home visit units. Results suggest that integrated home telemedicine services can support health professionals caring for patients with chronic disease, and improve their health. We have found that simple telemedicine services (ubiquitous access to ECPR, ECPR shared by care team, accessibility to case manager, problem reporting integrated in ECPR) can increase the number of patients that were not readmitted (51% intervention, 33% control), are acceptable to professionals, and involve low installation and exploitation costs. Further research is needed to determine the role of telemonitoring and televisit services for this kind of patients.