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Summary: An adaptation of the portfolio theory (PT) is proposed in this article, denoted as PrevPT, "Previsão" (in Portuguese) by PT, to integrate the three artificial neural networks, namely multilayer perceptron (MLP) backpropagation, radial basis function (RBF), and self‐organizing map (SOM), based forecasting techniques, aiming to analyze the impact of wind speed forecasting errors and achieve more accurate results. In its first use, the PT goal was to maximize a financial return, at any risk, through the diversification of securities or investments that are not positively correlated. Based on the development of PrevPT, which was used until this work only for solar forecasting, the proposed technique is applied in this paper to integrate and improve the results of individual wind forecasts. Four‐year wind speed data (January 2007 to December 2010) from two different locations (Algeciras, Spain and Petrolina, Brazil) were used. Our methodology develops a topology that integrates the forecasts obtained by MLP, RBF, and SOM aiming to obtain smaller forecast errors. By diversifying the forecasted asset, when one of the assets has negative prediction errors, another compensates for them and, thus, the total or partial cancellation of the errors occurs. PrevPT obtains a mean absolute percentage error of 1.13% for Spain and 2.35% for Brazil. PrevPT surpassed the results obtained by the three techniques applied individually in the two locations. The main innovations of the methodology are the significant reduction of errors and optimization of resource planning, and the beneficial features compared to other predictor integration techniques. [ABSTRACT FROM AUTHOR]

Summary: Brazil has one of the richest river basins in the world and an energy matrix based mostly on hydroelectric power. Current national policy is increasingly promoting renewable sources like wind power and solar photovoltaic power. Technologies used in generation energy via hydro, wind, and solar sources can be employed in decentralized generation. This study describes a turbine for hydro energetic use in flow sites with heads up to 2.5 (m). A hydrodynamic study was performed using computational fluid dynamics (CFD) analysis to evaluate the performance of this turbine. For this, a three‐dimensional model of the turbine was considered, consisting of three computational domains, using periodicity to decrease the computational cost. The variation of the angular velocity and the volumetric flow were the boundary conditions. The turbulence model used was the shear stress transport. The draft tube geometry was modified according to the results of the CFD analysis. The efficiency and performance of the turbine improved from 79% to 80% as a result of this modification. A case study was conducted for a comparative financial analysis of the turbine proposed in this paper, with wind power and solar photovoltaic decentralized generation in Brazil. [ABSTRACT FROM AUTHOR]

Summary: Considering Brazil an important player in the global market for the electricity production from solar sources and the recent investments in large power plants through public policies, this work evaluated a very large‐scale commercial photovoltaic power plant (VLS‐PVPP) performing an attributional life cycle assessment (LCA) in accordance with ISO 14040/44 standards. The comparative results obtained, for 18 impact categories, show that the use of global data tends to overestimate the impacts of energy production in the VLS‐PVPP implemented in Brazil, by at least double. When establishing scenarios for different regions of the Brazilian territory, the energy performance and emissions indicators used varied around 17% due to the high sensitivity of the solarimetric radiation index in the modeling. The carbon footprint ranged between 38.3 and 44.8 kgCO2eq/kWh, and the energy payback time (EPBT) ranged between 4.5 and 5.3 years. The fossil energy replacement ratio (FER) indicator was calculated to quantify the power plant sustainability, which, in the best scenario, showed that the system replenishes, with clean energy, 7.7 times the fossil energy used in its construction. The sensitivity study associated with the uncertainty analysis for the manufacturing location of photovoltaic modules showed little influence (about 1%) on these indicators, showing that the decision to import or manufacture PV modules does not involve an environmental bias, overturning this hypothesis. Furthermore, the sensitivity study of the photovoltaic modules showed that the multicrystalline silicon technology is still the best choice compared to the monocrystalline one, since the latter increased the environmental cost of the evaluated indicators by 20%. [ABSTRACT FROM AUTHOR]