Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics

Diretriz da Sociedade Brasileira de Cardiologia sobre Diagnóstico e Tratamento de Pacientes com Cardiomiopatia da Doença de Chagas

This guideline aimed to update the concepts and formulate the standards of conduct and scientific evidence that support them, regarding the diagnosis and treatment of the Cardiomyopathy of Chagas disease, with special emphasis on the rationality base that supported it.  Chagas disease in the 21st century maintains an epidemiological pattern of endemicity in 21 Latin American countries. Researchers and managers from endemic and non-endemic countries point to the need to adopt comprehensive public health policies to effectively control the interhuman transmission of T. cruzi infection, and to obtain an optimized level of care for already infected individuals, focusing on diagnostic and therapeutic opportunistic opportunities.   Pathogenic and pathophysiological mechanisms of the Cardiomyopathy of Chagas disease were revisited after in-depth updating and the notion that necrosis and fibrosis are stimulated by tissue parasitic persistence and adverse immune reaction, as fundamental mechanisms, assisted by autonomic and microvascular disorders, was well established. Some of them have recently formed potential targets of therapies.  The natural history of the acute and chronic phases was reviewed, with enhancement for oral transmission, indeterminate form and chronic syndromes. Recent meta-analyses of observational studies have estimated the risk of evolution from acute and indeterminate forms and mortality after chronic cardiomyopathy. Therapeutic approaches applicable to individuals with Indeterminate form of Chagas disease were specifically addressed. All methods to detect structural and/or functional alterations with various cardiac imaging techniques were also reviewed, with recommendations for use in various clinical scenarios. Mortality risk stratification based on the Rassi score, with recent studies of its application, was complemented by methods that detect myocardial fibrosis.  The current methodology for etiological diagnosis and the consequent implications of trypanonomic treatment deserved a comprehensive and in-depth approach. Also the treatment of patients at risk or with heart failure, arrhythmias and thromboembolic events, based on pharmacological and complementary resources, received special attention. Additional chapters supported the conducts applicable to several special contexts, including t. cruzi/HIV co-infection, risk during surgeries, in pregnant women, in the reactivation of infection after heart transplantation, and others.     Finally, two chapters of great social significance, addressing the structuring of specialized services to care for individuals with the Cardiomyopathy of Chagas disease, and reviewing the concepts of severe heart disease and its medical-labor implications completed this guideline.Esta diretriz teve como objetivo principal atualizar os conceitos e formular as normas de conduta e evidências científicas que as suportam, quanto ao diagnóstico e tratamento da CDC, com especial ênfase na base de racionalidade que a embasou. A DC no século XXI mantém padrão epidemiológico de endemicidade em 21 países da América Latina. Investigadores e gestores de países endêmicos e não endêmicos indigitam a necessidade de se adotarem políticas abrangentes, de saúde pública, para controle eficaz da transmissão inter-humanos da infecção pelo T. cruzi, e obter-se nível otimizado de atendimento aos indivíduos já infectados, com foco em oportunização diagnóstica e terapêutica. Mecanismos patogênicos e fisiopatológicos da CDC foram revisitados após atualização aprofundada e ficou bem consolidada a noção de que necrose e fibrose sejam estimuladas pela persistência parasitária tissular e reação imune adversa, como mecanismos fundamentais, coadjuvados por distúrbios autonômicos e microvasculares. Alguns deles recentemente constituíram alvos potenciais de terapêuticas. A história natural das fases aguda e crônica foi revista, com realce para a transmissão oral, a forma indeterminada e as síndromes crônicas. Metanálises recentes de estudos observacionais estimaram o risco de evolução a partir das formas aguda e indeterminada e de mortalidade após instalação da cardiomiopatia crônica. Condutas terapêuticas aplicáveis aos indivíduos com a FIDC foram abordadas especificamente. Todos os métodos para detectar alterações estruturais e/ou funcionais com variadas técnicas de imageamento cardíaco também foram revisados, com recomendações de uso nos vários cenários clínicos. Estratificação de risco de mortalidade fundamentada no escore de Rassi, com estudos recentes de sua aplicação, foi complementada por métodos que detectam fibrose miocárdica. A metodologia atual para diagnóstico etiológico e as consequentes implicações do tratamento tripanossomicida mereceram enfoque abrangente e aprofundado. Também o tratamento de pacientes em risco ou com insuficiência cardíaca, arritmias e eventos tromboembólicos, baseado em recursos farmacológicos e complementares, recebeu especial atenção. Capítulos suplementares subsidiaram as condutas aplicáveis a diversos contextos especiais, entre eles o da co-infecção por T. cruzi/HIV, risco durante cirurgias, em grávidas, na reativação da infecção após transplante cardíacos, e outros.    Por fim, dois capítulos de grande significado social, abordando a estruturação de serviços especializados para atendimento aos indivíduos com a CDC, e revisando os conceitos de cardiopatia grave e suas implicações médico-trabalhistas completaram esta diretriz.&nbsp

Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids

With continuous technological advances, increasing competitiveness of renewable sources, and concerns about the environmental impacts of the energy matrix, the use of hybrid microgrids has been promoted. These generation microsystems, historically composed basically of fossil fuels as the main source, have experienced an energy revolution with the introduction of renewable and intermittent sources. However, with the introduction of these uncontrollable sources, the technical challenges to system stability, low diesel consumption, and security of supply increase. The main objective of this work is to develop an operation and control strategy for energy storage systems intended for application in hybrid microgrids with AC coupling. Throughout the work, a bibliographic review of the existing applications is carried out, as well as a proposal for modification and combination to create a new control strategy. This strategy, based on optimized indirect control of diesel generators, seeks to increase generation efficiency, reduce working time, and increase the introduction of renewable sources in the system. As a result, there is a significant reduction in diesel consumption, a decrease in the power output variance of the diesel generation system, and an increase in the average operating power, which ensures effective control of hybrid plants

Case Study of Backup Application with Energy Storage in Microgrids

The reliability of energy supply is an important factor for end-users of electricity. Although many advances and efforts have been made by distribution companies to guarantee energy quality, weak feeders and grids are still usually found. As an alternative to minimize such problems, Battery Energy Storage Systems (BESSs) can be used to supply energy to users in the case of power outages or major energy quality problems. This paper presents test results on a real application scenario in a microgrid with different load configurations in the moment of interruption. The tests were compared to each other to analyze the impact found in each scenario. In addition to those, real unpremeditated cases of power quality problems were also discussed, and the performance of the utilized BESS was evaluated

Class Thresholds Pre-Definition by Clustering Techniques for Applications of ELECTRE TRI Method

The sorting problem in the Multi-criteria Decision Analysis (MCDA) has been used to address issues whose solutions involve the allocation of alternatives in classes. Traditional multi-criteria methods are commonly used for this task, such as ELECTRE TRI, AHP-Sort, UTADIS, PROMETHEE, GAYA, etc. While using these approaches to perform the sorting procedure, the decision-makers define profiles (thresholds) for classes to compare the alternatives within these profiles. However, most such applications are based on subjective tasks, i.e., decision-makers’ expertise, which sometimes might be imprecise. To fill that gap, in this paper, a comparative analysis using the multi-criteria method ELECTRE TRI and clustering algorithms is performed to obtain an auxiliary procedure to define initial thresholds for the ELECTRE TRI method. In this proposed methodology, K-Means, K-Medoids, Fuzzy C-Means algorithms, and Bio-Inspired metaheuristics such as PSO, Differential Evolution, and Genetic algorithm for clustering are tested considering a dataset from a fundamental problem of sorting in Water Distribution Networks. The computational performances indicate that Fuzzy C-Means was more suitable for achieving the desired response. The practical contributions show a relevant procedure to provide an initial view of boundaries in multi-criteria sorting methods based on the datasets from specific applications. Theoretically, it is a new development to pre-define the initial limits of classes for the sorting problem in multi-criteria approach

A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition

Particulate matter (PM) is one of the most harmful air pollutants to human health studied worldwide. In this scenario, it is of paramount importance to monitor and predict PM concentration. Artificial neural networks (ANN) are commonly used to forecast air pollution levels due to their accuracy. The use of partition on prediction problems is well known because decomposition of time series allows the latent components of the original series to be revealed. It is a matter of extracting the “deterministic” component, which is easy to predict the random components. However, there is no evidence of its use in air pollution forecasting. In this work, we introduce a different approach consisting of the decomposition of the time series in contiguous monthly partitions, aiming to develop specialized predictors to solve the problem because air pollutant concentration has seasonal behavior. The goal is to reach prediction accuracy higher than those obtained by using the entire series. Experiments were performed for seven time series of daily particulate matter concentrations (PM2.5 and PM10–particles with diameter less than 2.5 and 10 micrometers, respectively) in Finland and Brazil, using four ANNs: multilayer perceptron, radial basis function, extreme learning machines, and echo state networks. The experimental results using three evaluation measures showed that the proposed methodology increased all models’ prediction capability, leading to higher accuracy compared to the traditional approach, even for extremely high air pollution events. Our study has an important contribution to air quality prediction studies. It can help governments take measures aiming air pollution reduction and preparing hospitals during extreme air pollution events, which is related to the following United Nations sustainable developments goals: SDG 3—good health and well-being and SDG 11—sustainable cities and communities

Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble

The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances

Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS