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The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge's operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil. [ABSTRACT FROM AUTHOR]

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series. [ABSTRACT FROM AUTHOR]

The studies of spatial-temporal land use and land cover (LULC) change patterns, supported by future scenarios and simulation methods based on the assumption of natural socio-economic and territorial driving forces, allow us to go beyond an accurate diagnosis of the dynamics that have occurred so far, providing a picture of possible alternative futures, and are fundamental in assisting with the planning and policy-making in the territory. In this paper, we use LULC maps and explanatory variables aggregated in five dimensions (physical/natural, economic, sociocultural, technological, and demographic) to identify which are the main drinving forces in the evolution process and the simulation of LULC dynamics for 2036, using as a case study the Chapecó River ecological corridor (Chapecó EC) area. The Chapecó EC was created by the state government in 2010 with the goal of combining nature conservation with local and regional development. In this region, in the last two decades, the loss of areas of natural grassland and forest was on average five times higher than the average recorded in the state. Based on scenario-building methods using artificial neural networks, six predictive scenarios were elaborated, based on three socioeconomic scenarios (current conditions, growth, and socioeconomic recession) and two territorial intervention options (actions). This includes an action based on maintaining the current LULC, and another action of a conservationist nature with the recovery of forest and natural grassland areas to the proportions of areas found in 1990. The results indicate that if the current LULC is maintained, forest, pasture and agriculture areas tend to increase, while silviculture and natural grassland areas decrease, driven by economic and physical/natural driving forces. If there is a conservationist action, natural grassland and pasture areas tend to increase and silviculture and agriculture tend to lose area due to economic, technological, and physical/natural driving forces. These trends have revealed that the natural grassland preservation/restoration, the encouragement of conservationist agricultural practices combined with economic strategies, and the technological development of the rural sector seem to form the basis of economic development combined with biodiversity conservation. [ABSTRACT FROM AUTHOR]

Statistics show that an estimated 64 million people worldwide suffer from glaucoma. To aid in the detection of this disease, this paper presents a new public dataset containing eye fundus images that was developed for glaucoma pattern-recognition studies using deep learning (DL). The dataset, denoted Brazil Glaucoma, comprises 2000 images obtained from 1000 volunteers categorized into two groups: those with glaucoma (50%) and those without glaucoma (50%). All images were captured with a smartphone attached to a Welch Allyn panoptic direct ophthalmoscope. Further, a DL approach for the automatic detection of glaucoma was developed using the new dataset as input to a convolutional neural network ensemble model. The accuracy between positive and negative glaucoma detection, sensitivity, and specificity were calculated using five-fold cross-validation to train and refine the classification model. The results showed that the proposed method can identify glaucoma from eye fundus images with an accuracy of 90.0%. Thus, the combination of fundus images obtained using a smartphone attached to a portable panoptic ophthalmoscope and artificial intelligence algorithms yielded satisfactory results in the overall accuracy of glaucoma detection tests. Consequently, the proposed approach can contribute to the development of technologies aimed at massive population screening of the disease. [ABSTRACT FROM AUTHOR]

Featured Application: A hybrid model formed by neural network techniques and fuzzy systems that predicts rainfall and temperatures in the capital of Minas Gerais, Brazil, which contains several river sources essential for the Brazilian economy. The purpose of the paper is to construct a model capable of extracting knowledge from satellite data related to temperatures and rainfall forecast of the analyzed state. The forecast for rainfall and temperatures in underdevelope countries can help in the definition of public and private investment strategies in preventive and corrective nature. Water is an essential element for the economy and living things. This study had a main objective to use an intelligent hybrid model capable of extracting fuzzy rules from a historical series of temperatures and rainfall indices of the state of Minas Gerais in Brazil, more specifically in the capital. Because this is state has several rivers fundamental to the Brazilian economy, this study intended to find knowledge in the data of the problem to help public managers and private investors to act dynamically in the prediction of future temperatures and how they can interfere in the decisions related to the population of the state. The results confirm that the intelligent hybrid model can act with efficiency in the generation of predictions about the temperatures and average rainfall indices, being an efficient tool to predict the water situation in the future of this critical state for Brazil. [ABSTRACT FROM AUTHOR]

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners. [ABSTRACT FROM AUTHOR]

Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies. [ABSTRACT FROM AUTHOR]

Forecasting photovoltaic electricity generation is one of the key components to reducing the impacts of solar power natural variability, nurturing the penetration of renewable energy sources. Machine learning is a well-known method that relies on the principle that systems can learn from previously measured data, detecting patterns which are then used to predict future values of a target variable. These algorithms have been used successfully to predict incident solar irradiation, but the results depend on the specificities of the studied location due to the natural variability of the meteorological parameters. This paper presents an extensive comparison of the three ML algorithms most used worldwide for forecasting solar radiation, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), aiming at the best prediction of daily solar irradiance in a São Paulo context. The largest dataset in Brazil for meteorological parameters, containing measurements from 1933 to 2014, was used to train and compare the results of the algorithms. The results showed good approximation between measured and predicted global solar radiation for the three algorithms; however, for São Paulo, the SVM produced a lower Root-Mean-Square Error (RMSE), and ELM, a faster training rate. Using all 10 meteorological parameters available for the site was the best approach for the three algorithms at this location. [ABSTRACT FROM AUTHOR]

The availability of freshwater is becoming a global concern. Because agricultural consumption has been increasing steadily, the mapping of irrigated areas is key for supporting the monitoring of land use and better management of available water resources. In this paper, we propose a method to automatically detect and map center pivot irrigation systems using U-Net, an image segmentation convolutional neural network architecture, applied to a constellation of PlanetScope images from the Cerrado biome of Brazil. Our objective is to provide a fast and accurate alternative to map center pivot irrigation systems with very high spatial and temporal resolution imagery. We implemented a modified U-Net architecture using the TensorFlow library and trained it on the Google cloud platform with a dataset built from more than 42,000 very high spatial resolution PlanetScope images acquired between August 2017 and November 2018. The U-Net implementation achieved a precision of 99% and a recall of 88% to detect and map center pivot irrigation systems in our study area. This method, proposed to detect and map center pivot irrigation systems, has the potential to be scaled to larger areas and improve the monitoring of freshwater use by agricultural activities. [ABSTRACT FROM AUTHOR]

This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might be useful for wind power generation; for example, they could acquire important information on how the local wind potential can be usable for a possible wind power plant through understanding future wind speed values. To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation). Thus, it was possible to consider the meteorological characteristics of the study regions. It is possible to verify the hybrid models' efficiency in providing perfect adjustments to the observed data. This statement is based on the low values found in the error statistical analysis, i.e., an error of approximately 5.0% and a Nash-Sutcliffe coefficient near to 0.96. These results were certainly important in predicting the wind speed time-series, which was similar to the observed wind speed time-series profile. Great similarities of maximums and minimums between the series were evident and showed the capacity of the models to represent the seasonality characteristics. [ABSTRACT FROM AUTHOR]

The determination of the orthometric height from geometric leveling has practical difficulties that, despite a number of scientific and technological advances, passed a century without substantial modifications or advances. Currently, the Global Navigation Satellite System (GNSS) has been used with reasonable success for orthometric height determination. With a sufficient number of benchmarks with known horizontal and vertical coordinates, it is often possible to adjust using the least squares method mathematical expressions that allow interpolation of geoid heights. The objective of this study is to present an alternative method to interpolate geoid heights based on the technique of Artificial Neural Networks (ANNs). The study area is the Brazilian state of São Paulo, and for training the ANN the authors have used geoid height information from the EGM08 gravity model with a grid spacing of 10 minutes of arc. The efficiency of the model was tested at 157 points with known geoid heights distributed across the study area. The results were also compared with the Brazilian Geoid Model (MAPGEO2004). Based on those 157 benchmarks it was possible to verify that the model generated by ANNs provided a mean absolute error of 0.24 m in obtaining a geoid height value. Statistical tests have shown that there was no difference between the means from known geoid heights and geoid heights provided by the neural model for a significance level of 5%. It was also found that ANNs provided an improvement of 2.7 times in geoid height estimates when compared with the MAPGEO2004 geoid model. [ABSTRACT FROM AUTHOR]

Food production to meet human demand has been a challenge to society. Nowadays, one of the main sources of feeding is soybean. Considering agriculture food crops, soybean is sixth by production volume and the fourth by both production area and economic value. The grain can be used directly to human consumption, but it is highly used as a source of protein for animal production that corresponds 75% of the total, or as oil and derived food products. Brazil and the US are the most important players responsible for more than 70% of world production. Therefore, a reliable forecasting is essential for decision-makers to plan adequate policies to this important commodity and to establish the necessary logistical resources. In this sense, this study aims to predict soybean harvest area, yield, and production using Artificial Neural Networks (ANN) and compare with classical methods of Time Series Analysis. To this end, we collected data from a time series (1961–2016) regarding soybean production in Brazil. The results reveal that ANN is the best approach to predict soybean harvest area and production while classical linear function remains more effective to predict soybean yield. Moreover, ANN presents as a reliable model to predict time series and can help the stakeholders to anticipate the world soybean offer. [ABSTRACT FROM AUTHOR]

Deforestation is one of the major threats to natural ecosystems. This process has a substantial contribution to climate change and biodiversity reduction. Therefore, the monitoring and early detection of deforestation is an essential process for preservation. Techniques based on satellite images are among the most attractive options for this application. However, many approaches involve some human intervention or are dependent on a manually selected threshold to identify regions that suffer deforestation. Motivated by this scenario, the present work evaluates Deep Learning-based strategies for automatic deforestation detection, namely, Early Fusion (EF), Siamese Network (SN), and Convolutional Support Vector Machine (CSVM) as well as Support Vector Machine (SVM), used as the baseline. The target areas are two regions with different deforestation patterns: the Amazon and Cerrado biomes in Brazil. The experiments used two co-registered Landsat 8 images acquired at different dates. The strategies based on Deep Learning achieved the best performance in our analysis in comparison with the baseline, with SN and EF superior to CSVM and SVM. In the same way, a reduction of the salt-and-pepper effect in the generated probabilistic change maps was noticed as the number of training samples increased. Finally, the work assesses how the methods can reduce the time invested in the visual inspection of deforested areas. [ABSTRACT FROM AUTHOR]

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option. [ABSTRACT FROM AUTHOR]