Search

A good solar power photovoltaic generation forecast depends on good quality time series data from measurements of Global Horizontal Irradiance and Solar Power Generation. However, measurement system failures and errors in data handling can corrupt data records with gaps and outliers that undermine forecasting accuracy. Therefore, it is important that the fitting of solar energy prediction models must be preceded by a data analysis in order to detect and correct measurement errors. Given that Global Horizontal Irradiance and Solar Power Generation are correlated variables, this paper aims to present the main characteristics of an offline approach developed for the joint treatment of hourly data values of both variables in a photovoltaic plant. In the proposed methodology, the measurements of Global Horizontal Irradiance and Solar Power Generation are analyzed by using reanalysis data and statistical and data mining techniques for the correction of outliers and the filling of data gaps. The application of the approach is illustrated by the analysis of measurements from a real solar PV system.

This paper presents an efficient approach to the Supervised MDS method. This method handles the problems of data visualization, supervised classification and bipartite ranking. In order to overcome the non-differentiable nature of the Supervised MDS method, the mathematical formulation proposed in this work is based on the hyperbolic smoothing technique. The performance of the algorithm is evaluated by computational experiments. The results show that the proposed methodology presented, in most cases, better results than the results available in the literature. Furthermore, the methodology presents a good performance in relation to the methods Logistic regression, Naive Bayes and Support Vector Machine.

Este trabalho objetiva comparar as acurácias obtidas para cada um dos seguintes classificadores: análise discriminante (LDA), regressão logística (RL), redes neurais – multilayer perceptron (MLP) e máquinas de vetores de suporte (SVM), em diferentes períodos. Com a globalização da economia, o aumento da disponibilidade de informações financeiras e o livre fluxo de capitais ao redor do mundo, investidores, profissionais de mercado, acadêmicos, dentre outros usuários da informação contábil, vêm se preocupando em saber se empresas estão com dificuldades financeiras. A pesquisa realizada é descritiva e parte de uma abordagem quantitativa. As variáveis independentes utilizadas correspondem a um conjunto de indicadores financeiros calculados com base em dados oriundos de demonstrações financeiras de companhias listadas na BM&FBOVESPA. A seleção das variáveis foi feita por meio do método stepwise e algoritmo genético. Nos estudos realizados, a regressão logística apresentou o melhor desempenho nos grupos de teste de 2015 e 2016. Essa pesquisa contribuiu para preencher um gap em relação à utilização de dois grupos de treinamento para averiguar se os dados contábeis do ano de implementação plena das IFRS (2010) no Brasil podem ter impactado as acurácias dos classificadores.

Após a consolidação dos movimentos de abertura econômica das nações e o surgimento da teoria de globalização nas décadas de 80 e 90, diversas consultorias e instituições de pesquisas passaram a se interessar pela análise da competitividade, como um dos indicadores para o desenvolvimento econômico. No Brasil, o Ranking de Competitividade dos Estados vem sendo elaborado pelo Centro de Liderança Pública – CLP desde 2011, com a finalidade de promover uma maior compreensão das 27 unidades da federação, trazendo para o público um instrumento para orientar a atuação dos líderes públicos na melhoria da competitividade e da gestão de seus Estados. O Ranking 2019 é determinado com base em 69 indicadores, distribuídos em 10 pilares temáticos. O objetivo do presente artigo consiste em identificar como foram determinados os pesos para os 10 pilares que compõem o Ranking 2019 e identificar quantitativamente os pesos atribuídos aos indicadores no cômputo geral.Palavras-chave: Estados brasileiros, gestão pública, ranking de competitividade, indicadores.

DOI: 10.12957/cadest.2020.55395 Propoe-se uma abordagem para modelar a incerteza da producao eolica mensal no problema de despacho de operacao, aderente ao algoritmo de solucao utilizado (PDDE) no modelo NEWAVE. A abordagem compreende: (i) agrupamento estatistico dos regimes de ventos; (ii) avaliacao de funcoes de transferencia mensais entre ventos e producao eolica; (iii) geracao de series sinteticas de ventos correlacionadas as afluencias aos reservatorios das hidroeletricas; e (iv) representacao das producoes eolicas mensais no algoritmo PDDE do NEWAVE. Resultados iniciais com configuracoes reais do sistema interligado brasileiro apontam para a sua efetividade.

Dengue is a re-emerging disease, currently considered the most important mosquito-borne arbovirus infection affecting humankind, taking into account both its morbidity and mortality. Brazil is considered an endemic country for dengue, such that more than 1,544,987 confirmed cases were notified in 2019, which means an incidence rate of 735 for every 100 thousand inhabitants. Climate is an important factor in the temporal and spatial distribution of vector-borne diseases, such as dengue. Thus, rainfall and temperature are considered macro-factors determinants for dengue, since they directly influence the population density of Aedes aegypti, which is subject to seasonal fluctuations, mainly due to these variables. This study examined the incidence of dengue fever related to the climate influence by using temperature and rainfall variables data obtained from remote sensing via artificial satellites in the metropolitan region of Rio de Janeiro, Brazil. The mathematical model that best fits the data is based on an auto-regressive moving average with exogenous inputs (ARMAX). It reproduced the values of incidence rates in the study period and managed to predict with good precision in a one-year horizon. The approach described in present work may be replicated in cities around the world by the public health managers, to build auxiliary operational tools for control and prevention tasks of dengue, as well of other arbovirus diseases.

O trabalho discute o uso de métodos estatísticos de previsão e o uso da análise envoltória de dados como auxílio na definição de metas para indicadores de sustentabilidade no contexto da Agenda 2030. A metodologia proposta consiste no tratamento das séries temporais com a identificação e remoção de outliers a partir da aplicação do método Loess, na realização das previsões de acordo com os modelos de suavização exponencial, na definição do benchmark a partir da Análise Envoltória de Dados (DEA) e posterior definição da meta global e, por último, na distribuição da meta pelo método de mínimos quadrados aliando o benchmark com as previsões. A metodologia foi aplicada em uma empresa do setor elétrico brasileiro para os indicadores de consumo de energia elétrica e consumo de água da rede de abastecimento das atividades administrativas.

A introdução da energia eólica tem demandado o aperfeiçoamento das práticas e metodologias utilizadas na operação e no planejamento do Sistema Interligado Nacional (SIN). Contudo, apesar da participação expressiva da geração eólica no SIN, a divulgação pública de dados de aproveitamentos eólicos no SIN ainda é limitada, em especial, a disponibilização de registros anemométricos. Para mitigar os efeitos de tal limitação deve-se considerar dados de reanálises. Neste artigo, apresentam-se os resultados de um estudo realizado a partir de dados de reanálises oriundos do MERRA-2 (global) para estimação de fatores de capacidade, em bases horárias e mensais, no Nordeste brasileiro.

A good solar power photovoltaic generation forecast depends on good quality time series data from measurements of global horizontal irradiation and solar generation. However, measurement system failures and errors in data handling can corrupt data records with gaps and outliers that undermine forecasting accuracy. Therefore, it is important that the fitting of solar energy prediction models must be preceded by a data analysis in order to detect and correct measurement errors. This paper presents the main features of an approach for the joint data cleaning of solar photovoltaic generation and solar irradiation. The methodology comprises 5 chained steps and consists in the combined treatment of global horizontal irradiation and solar photovoltaic generation data using statistical techniques, data mining algorithms and reanalysis data with the purpose of correcting outliers, replacing incorrect values and filling data gaps. The application of the proposed approach is illustrated with a real system presenting good performance.

This paper presents the determination of diversity factors related to the increased demand when charging EVs, electric vehicles, and focuses on the adequacy of distribution network capacity. The work aimed to meet the R&D project "Smart City Buzios" proposed by ENEL Distribuicao Rio within the scope of the National Electric Energy Agency - ANEEL. The treated results of several measurements of EV charging characteristics provided the basis to estimate the increase in electricity demand. Deterministic and stochastic analyzes considering the influence of the distance previously traveled by the vehicle and the moment of its connection to the network were compared, allowed to obtain the diversity factor. This study shows the influence of the connection moment to recharge EVs in predicting the capacity of transformers as well as the need for their stochastic treatment. Therefore, the results recommended implementing communication systems to offer attractive tariffs aiming at shifting the recharging of EVs to daily times of lower demand.

The theft of electrical energy is one of the main problems faced by electricity distribution utilities, especially in developing countries. Aware of the difficulties in combating non-technical losses (NTLs) in Brazil, the National Electric Energy Agency (ANEEL) established tolerable limits for the percentage of non-technical losses to each Brazilian distribution utility. Despite the notable progress made by ANEEL, when comparing public utility NTLs and their regulatory targets in the last decade, it was observed that the goals defined by this agency were not able to lead to a general reduction in NTLs in the country. Thus, the search for alternative methodologies to deal with the topic is necessary. A more attractive alternative to the ANEEL’s model is an efficient frontier model. This paper describes a stochastic frontier cost model for panel data whose equation is specified to provide the tolerable limits for the percentage of NTLs. The proposed model was applied to a panel of data containing annual observations, over 10 years, of 41 distribution utilities in the Brazilian electrical system.

O presente estudo buscou desenvolver um diagnostico da harmonizacao das estruturas de controle interno na Marinha do Brasil (MB) com a abordagem preconizada pelo Committee of Sponsoring Organizations of the Theadway Commission (COSO). Dessa forma, a pesquisa foi definida como descritiva, com abordagem quantitativa. Realizou-se a coleta de dados por meio de um questionario estruturado e foram empregadas tecnicas estatisticas multivariadas para analise de dados. Os resultados revelam que a maioria das Organizacoes Militares (OM) avaliadas apresentou boa harmonizacao com a abordagem COSO.

The regulatory power sector model in several countries determines tariff review forms based, among other things, on sharing efficiency gains with consumers. As these reviews have an important impact on consumers and distribution utilities, it is necessary that the adopted methodologies always be improved. To this end, this article assessed a Bayesian inference application in order to estimate a stochastic cost frontier considering temporal efficiency dynamics. Taking this point into consideration is essential, since studies carried out to assess power sector efficiency have neglected the fact that part of efficiency increases originate from scale gain due to market expansion, which occurs over time. The sample assessed herein is composed of panel data from 61 electric power utilities between 2003 and 2016. The results demonstrate that the tariff review is positively affected by distributor efficiency.

This paper presents a stochastic simulation model to evaluate the recharging of electric vehicles in power distribution networks, focusing on the adequacy of the capacity of transformers and their feeder. The model includes the effects of random variables related to distances previously traveled and connection times of electric vehicles to the grid.

Business associations often promote forums between entrepreneurs. One strategy for establish contacts among participants is to organize them into small groups assigning them to tables so that participants in the same group can present their companies and projects. After an interval, a new round begins with new groups in order to establish new contacts. The repetition of this strategy allows each participant to expand his/her business network, but the repeated contacts between participants should be avoided. We present an Integer Linear Programming model that minimizes the repeated contacts when the participants are successively assigned to new groups. The model was implemented in R programming language and it has been applied to organize business forums. In order to illustrate the model application, this paper shows the results from a case study with 108 participants.

Energy models consider technical and economic criteria to define the optimal energy mix to produce electricity. However, the results are sub-optimal from the social point of view, since they do not include socio-environmental factors. The purpose of this study is to access the sustainability of power plants considering technical-economic, socio-environmental and institutional aspects so that to improve the expansion planning. Thus, the efficiencies calculated by slacks based measure approach of Data Envelopment Analysis methodology can be implemented as constraints in the models, signalizing that the socio-environmental impact cannot exceed a limit, which can be calculated by the energy model itself. The results show that renewable power plants contribute more to allocative efficiency than fossil fuel ones, both in terms of social and environmental aspects, which are of utmost importance in ranking power plants to model power capacity expansion. Considering just technical and economic factors has proved to be insufficient to optimize the system in terms of allocative efficiency.

The energy companies are always facing the challenge of producing more accurate load forecasts. A fuzzy logic methodology is proposed in order to extract rules from the input variables and provide Brazil's long-term annual electricity demand forecasts. In recent literature, the formulation of these types of models has been limited to treating the explanatory variables in the univariate form, or involving only the GDP. This study proposes an extension of this model, starting with population and the GDP additional value. The proposed model is compared with the official projections. The obtained results are quite promising.

In Brazil, the transmission assets are remunerated for their availability regardless of the full utilization of their capacities. The annual allowed revenue for each transmission utility is determined by the Brazilian Electricity Regulatory Agency (ANEEL). ANEEL has been using Data Envelopment Analysis (DEA) to define the regulatory operational expenditure (OPEX) for each utility, a key element for the annual allowed revenue assessment. Based on the ANEEL's DEA model this work proposes improvements in representing the quality of service, transmission capacity and transmission network length in the DEA model. Furthermore, this paper presents a more robust methodology to evaluate the regulatory operational expenditure of transmission utilities by applying several distinct DEA models; then a global efficiency score is defined by the geometric mean of the efficiency scores resulting from the set of DEA models analyzed. The performance of the proposed approach is illustrated with real data of the Brazilian transmission utilities.

In this paper we estimate, analyze and predict short-term non-technical loss (NTL) of electric power of Brazilian energy service companies based on different assumptions for the covariance structure of the errors and controlling for socio-economic confounding variables. Although the correlation among repeated responses is not usually of intrinsic interest, it is an important aspect of the data that must properly be accounted for to produce valid inferences in longitudinal or panel data analysis. In the extended linear mixed effects model, the covariance matrix of the response vector is comprised by two subcomponents, a random effect component that can represent between group variation and a intraclass or within group component. So, in order to adequately treat the longitudinal character of NTL data, we use the decomposition of these variance components to evaluate different architectures to the within group errors. Using data of 59 Brazilian distributing utilities from 2004 to 2012, we fit a conditionally independent errors model and three other models with autoregressive-moving average parametrization to the intraclass disturbances. Finally, we compare models using the MAD and MAPE metrics in the prediction of NTL for the year of 2013. The findings suggest that the approach can be satisfactorily implemented in future statistical analysis of NTL.

The increasing penetration of wind power generation requires the improvement of the computational tools that support decision making in the operation and planning of electric power systems. This paper describes an approach to properly model and measure the impact of wind farms speed correlations on probabilistic power flow figures. For this, the Nataf transformation is applied to accurately construct wind power scenarios, compatible with the spatial correlations between the wind regimes, under a Monte Carlo simulation scheme. The proposed methodology is applied to the modified IEEE 24-bus Test System, comprising deterministic and probabilistic case studies, also considering null, positive and negative correlations. The results obtained highlight the ability of the methodology to capture and quantify the portfolio effect of different wind farms in the operation of the electric network.

Power system planning demands a huge computational effort and to mitigate this some simplifications are needed, among them the use of approximated load curves or typical daily load profiles expressed by a small number of load levels (e.g., heavy, medium and light). This paper presents a methodology to build typical daily load profiles useful to the expansion and operational planning studies. The proposed approach is based on statistical clustering techniques and on exploratory data analyses (EDA), which are preceded by a filtering procedure. It is also proposed the creation of LoadPlot curves, which consist of putting in the same curve boxplots for each hour of the day together with the aggregated levels. The proposed methodology is illustrated by using real data of the Brazilian interconnected electric power system.

Data quality is critical in the short-term load forecasting. Often, load data show outliers, discontinuities, and gaps resulting from abnormal operation of the electrical power system or failures and problems in the measurement system. The presence of corrupted data impairs the specification of load prediction models and consequently affects the quality of the predictions obtained. Therefore, the construction of a load prediction model must be preceded by data cleaning. This work presents a methodology based on statistical methods and data mining techniques for load data cleaning. The application of the proposed methodology is illustrated by results from computational experiments conducted with load data from the Brazilian Interconnected Power System (BIPS).

The increasing wind power generation requires the updating of the computational tools that support decision making in the operation and planning of electric power systems. This work describes a methodology to model the spatial correlations among wind speeds probability distributions in probabilistic power flow. The proposed methodology is based on Nataf transformation and Monte Carlo Simulation. The proposed methodology is illustrated in case studies with the IEEE 24 bus test system, highlighting the importance of adequately considering such correlations.

This paper proposes a method (denoted by WD-ANN) that combines the Artificial Neural Networks (ANN) and the Wavelet Decomposition (WD) to generate short-term global horizontal solar ra- diation forecasting, which is an essential information for evaluating the electrical power generated from the conversion of solar energy into electrical energy. The WD-ANN method consists of two basic steps: firstly, it is performed the decomposition of level p of the time series of interest, generating p + 1 wavelet orthonormal components; secondly, the p + 1 wavelet orthonormal components (generated in the step 1) are inserted simultaneously into an ANN in order to generate short-term forecasting. The results showed that the proposed method (WD-ANN) improved substantially the performance over the (traditional) ANN method.

O aumento da participacao da energia eolica requer a atualizacao das ferramentas computacionais que suportam os processos de tomada de decisao na operacao e no planejamento de sistemas eletricos. O presente trabalho descreve uma metodologia para modelar as correlacoes espaciais entre as velocidades de vento no problema de fluxo de potencia probabilistico. A metodologia proposta baseia-se na transformacao de Nataf e na Simulacao de Monte Carlo. Adicionalmente, a relacao estocastica entre a velocidade do vento e a potencia gerada e modelada por meio da estimacao nao parametrica da densidade de probabilidade condicional. A metodologia proposta e ilustrada por meio de estudos de caso com o Sistema Teste IEEE 24 barras Modificado.

This paper shows efficiency indices for 60 Brazilian electricity distribution utilities. The efficiency scores are gauged by three DEA models. For both models, these quantities are evaluated under different contexts. One treats with respect to the regulator perspective. The others examine an alternative approach based on cluster analysis and restrictions on factor weights. It is worth pointing out that these developments can reduce the information asymmetry and improve the regulator's skill to compare the performance of the utilities, a fundamental in incentive regulation schemes.

No terceiro ciclo de revisão tarifária, a Agência Nacional de Energia Elétrica (Aneel) adotou uma metodologia baseada na Análise Envoltória de Dados (DEA) para a definição dos níveis eficientes dos custos operacionais das distribuidoras. Neste trabalho são apresentadas propostas que aprimoram a metodologia da Aneel. Em particular, propõe-se a segmentação do conjunto de distribuidoras por técnicas de análise de agrupamentos, com a finalidade de estabelecer comparações justas entre as empresas. Adicionalmente, visando obter índices de eficiência que levem em conta as avaliações realizadas pelos pares de cada empresa, propõe-se o uso da avaliação cruzada na definição dos níveis eficientes dos custos operacionais.

The aim of this paper is to propose a new methodology for hydroelectric energy forecasting. A new approach for selection of the number of eigenvalues in SSA is also proposed. In this paper it is proposed the hierarchical clustering associated to PCA and integrated to ARIMA models. The proposed approach is applied to forecast the affluent flow in a hydroelectric plant located at Parana River Basin, Brazil. As a matter of fact, modeling such series is quite important for the optimal dispatch of the energy generation in Brazil due to the heavy participation of hydro plants in the country (over 85% of the generated energy comes from hydro plants).

The forecasting of electricity consumption and demand plays a pivotal role in electric power systems planning. This paper proposes the combination of forecasts from two approaches with the aim of improving the forecasting accuracy, in order to make the best use of the installed transmission and generating capacity. In the first approach, the consumption time series is decomposed by wavelet analysis and a Box-Jenkins model is fitted to each wavelet component, following which the individual components forecasts are added to compute the total consumption forecast. The alternative approach, uses the Singular Spectrum Analysis technique to model the consumption time series in order to shrink the noise level. Thereafter, the Box-Jenkins model is used to forecast the filtered time series, producing a second forecast for the consumption series. Eventually, the two forecasts are combined geometrically in order to minimize the mean square error. The proposed methodology is illustrated by a computational experiment with the time series of residential consumption of electricity in Brazil.

The non-payment rate has been one of the main problems faced by the electric power distribution utilities. It is therefore essential that the utilities provide a methodology to assess the capability of a residential consumer to pay on time his/her electricity bill. As such, the utility will be able to simulate strategies and mitigate the risk of non-payment. This article presents a proposal based on fuzzy logic approach to compute a “Payment Capability Index” PCI for residential customers. A spatial view of PCI is provided by illustrative maps obtained by Geographic Information System GIS.