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Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows. [ABSTRACT FROM AUTHOR]

Mitigating the harmful effects of flooding is essential in the current climate change and urban growth scenario, where these natural phenomena are expected to occur more often. Addressing this problem, we present a novel graph-based forecasting model for predicting urban flooding and showcase its application in the Bow River in the City of Calgary, Alberta, Canada. The proposed model is based on graph theory and deep learning paradigms and was used to forecast flooding occurrences up to 24 h ahead. Our new approach developed the SAGE algorithm, a hybrid learning and planning approach that accurately predicted floodings for various analyzed forecasting horizons, increasing the forecasting performance by up to 44% compared to the persistence model. The SAGE model also returned superior or competitive results compared to other models in the literature. The superiority of the SAGE model was highlighted for longer forecast horizons of 24 h ahead, where it reached a maximum improvement of 79%, suggesting its superiority in capturing spatiotemporal information in the dataset. These results indicate that the SAGE approach is a cutting-edge tool for flooding forecasting, and it could be used to develop early flood warning systems to reduce potential flooding impacts. [ABSTRACT FROM AUTHOR]

In this work, the catalytic performance of clinoptilolite (CLIN) and SBA-15 catalysts, doped with Fe and Cu, was evaluated in the selective catalytic reduction of NO using NH3 as a reducing agent (SCR-NH3). Both Cu-CLIN and Fe-CLIN were obtained by ion-exchange using natural clinoptilolite zeolite originating from the Hrabovec deposit (northeast Slovakia region). Cu-SBA-15 and Fe-SBA-15 were prepared by impregnation into SBA-15 mesoporous synthesized silica. Standard catalytic activity tests were carried out on a bench-scale laboratory apparatus using a reaction mixture of a standard test. GHSV of 48,000 h−1 was adopted based on the space velocity of a real NH3-SCR catalyst for diesel vehicles (100–550 °C). All Cu-doped samples showed better NO conversion values than Fe-doped samples. Clinoptilolite catalysts were more active than those based on SBA-15. Maximum NO conversions of about 96% were observed for Cu-CLIN and Fe-CLIN at 350–400 °C, respectively. Moreover, Fe-CLIN also showed higher stability in the presence of SO2 and water steam at 350 °C. These results demonstrate the potential of metal-doped natural clinoptilolite to be used as cost-effective catalysts applied to the abatement of NOx emissions generated in automotive combustion processes. [ABSTRACT FROM AUTHOR]

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This work introduces a new approach for the evaluation of numerical methods used in calculating the Weibull parameters for the prediction of wind resource. In this evaluation, power is a key issue since it proves to be quite relevant in predicting the feasibility of a given site for efficient use of wind energy. Previous knowledge of such information is important for the decision process on the technical feasibility of installing industrial wind farms. The paper performed a statistical analysis of seven mathematical methods to estimate the shape parameter k and the scaling parameter c of the Weibull distribution. Wind speed and wind power data collected in two coastal cities (Icapuí and Camocim), State of Ceará, in the northeast region of Brazil were used. The methods used in the development of this research are the graphical method, maximum likelihood method, modified maximum likelihood method, empirical method, moment method, energy pattern factor method and the equivalent energy method. Comparative analysis of efficiency and accuracy involves the application of the following statistical tests: analysis of variance (R2), root mean square error (RMSE), and chi-square (X 2). The use of power as a parameter to be evaluated in this study by analyzing the Weibull distribution, is something different from what has been discussed in the literature, and indicates that methods which are well evaluated when the wind speed is used as a comparison factor can change position when ranked by power. It is important to emphasize that the values of k and c were calculated directly from the values of wind power (not the wind speed, as usual), and subsequently it was statistically tested the adequacy of the values in the prediction of wind speed and wind power. The presented results indicate that the values generated for the shape and scale parameters provided differences considering the databases formed by wind speed and wind power, namely the graphical method. The wind power data produced accurate results when submitted to statistical tests. [ABSTRACT FROM AUTHOR]