Search

The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun's path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm. [ABSTRACT FROM AUTHOR]

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) pandemic has had social and clinical effects over the healthcare system. Globally, over 314.019.135 confirmed cases and 5.507.370 deaths have been recorded at 12 January 2022, based on the Dashboard updated by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The continuous rise in the number of asymptomatic, pre-symptomatic, and symptomatic patients has developed efficient and accessible models for prediction using Open Source Libraries and Cloud Services. This paper proposes different machine-learning algorithms to analyse the COVID-19 OpenData Resources from Mexico and Brazil, which represent the two major populations, economics and affected by disease countries in Latin America. This model uses only the COVID-19 patient's geographical, social conditions, economics conditions, clinical risk factors, symptom reports, and demographic data to predict recovery and death. The model of Mexico has an accuracy of 93% and the perceived mean of recall and the precision (F1 Score) of 0.79 on the dataset used. On the other hand, the model of Brazil has an accuracy of 69% and F1 score of 0.75 on the dataset studied. The result considers data from patients under the age of 0 and 120 years. The contribution of the work is the application of Big Data technologies and Machine Learning algorithms using Open Resource Libraries and Amazon Web Services (AWS) with the vision to improve the clinical diagnosis, even infectious disease with mathematical approaches. [ABSTRACT FROM AUTHOR]

Electricity generation in Brazil heavily depends on hydroelectric power, making it vulnerable to fluctuations due to its reliance on weather patterns. Accurately forecasting water inflow into hydroelectric plants is vital for the National Electric System Operator to make decisions regarding the monthly scheduling and operation of the power system. In this paper, an evaluation of predicted flows for a 14-day horizon are evaluated for the Tucuruíhydroelectric plant, located in the Tocantins river in the North of Brazil. The temporal fusion transformer (TFT), long short-term memory (LSTM), and temporal convolutional networks (TCN) are compared. The findings demonstrate that the TFT is a more suitable alternative than LSTM and TCN models for predicting inflows for the next 14 days. The TFT model is hypertuned by Optuna to achieve an optimized structure (h-TFT). The h-TFT had a mean absolute percentage error of 13.1 and a Nash–Sutcliffe of 0.96, outperforming its initial version and even the bidirectional LSTM model in benchmarking. Based on the error results, h-TFT showed promise for flow forecasting and provides insights into decision-making processes in the Brazilian electricity sector. • Hypertuning the TFT improves its performance in median-term forecasting. • The h-TFT outperforms LSTM, and TCN models for time series forecasting. • Deep learning applied for scheduling operation of the electrical power system. • Energy planning based on the inflow forecasting of hydroelectric power plants. [ABSTRACT FROM AUTHOR]