Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values.

Dried, bitter orange leaves are widely used because of their nutritious and medicinal applications. As a result, many technologies have been used to accomplish its drying process. However, drying needs a long time and high energy demand, especially in humid climates. In this paper, bitter orange leaf drying was carried out using thermal and photovoltaic solar energy (integrated system, IS), eliminating the high humidity inside of the drying chamber to improve this process. A regular solar dryer (RD) was also used to compare the kinetics, mathematical modeling, and colorimetry study (as a quality parameter), evaluating both systems’ performances. The drying leaves’ weights were stabilized after 330 min in the RD and after 240 min in the IS, with a maximum drying rate of 0.021 kg water/kg dry matter∙min, reaching a relative humidity of 7.9%. The Page and Modified Page models were the best fitting to experimental results with an Ra² value of 0.9980. In addition, the colorimetric study showed a better-preserved color using the IS, with an ∆E of 9.12, while in the RD, the ∆E was 20.66. Thus, this system implementation can reduce agroindustry costs by reducing time and energy with a better-quality and sustainable product, avoiding 53.2 kg CO₂ emissions to the environment.

Traffic emission is one of the most severe issues in our modern societies. A large part of emissions occurs in cities and especially at intersections due to the high interactions between vehicles. In this paper, we proposed a cellular automata model to investigate the different traffic emissions (CO${}_2$, PM, VOC, and NO${}_x$) and speeds at a two-lane signalized intersection. The model is designed to analyze the effects of signalization by isolating the parameters involved in vehicle-vehicle interactions (lane changing, speed, density, and traffic heterogeneity). It was found that the traffic emission increases (decreases) with the increasing of green lights duration ($T_g$) at low (high) values of vehicles injection rate ($\alpha$). Moreover, by taking CO${}_2$ as the order parameter, the phase diagram shows that the system can be in four different phases (I, II, III, and IV) depending on $\alpha$ and $T_g$. The transition from phase II (I) to phase III (II) is second order, while the transition from phase II to phase IV is first order. To reduce the traffic emission and enhance the speed, two strategies were proposed. Simulation results show a maximum reduction of 13.6% in vehicles’ emissions and an increase of 9.5% in the mean speed when adopting self-organizing intersection (second strategy) at low and intermediate $\alpha$. However, the first strategy enhances the mean speed up to 28.8% and reduces the traffic emissions by 3.6% at high $\alpha$. Therefore, the combination of both strategies is recommended to promote the traffic efficiency in all traffic states. Finally, the model results illustrate that the system shows low traffic emission adopting symmetric lane-changing rules than asymmetric rules.

The analysis of the behavior of lithium-ion batteries has gained considerable interest in recent years. There are different alternatives for the analysis of this behavior; however, depending on the type of modeling, there are application and optimization restrictions. In this work, a hybrid model has been made that is capable of predicting the characteristics of a lithium-ion battery. As a novelty, the simplification, at the same time, facilitates the sampling of parameters for their prompt selection for optimization. A new model open to the user is proposed, which has proven to be efficient in simulation time. For example, one hour simulates it in 5 min, providing information detailing how these parameters, State of Health (SOH), Open Circuit Voltage (VOC), State of charge (SOC), and Number of charge and discharge cycles, in the face of temperature variations and charge and discharge cycles. Opening the possibility of optimizing the parameters with different techniques to estimate the performance and dynamics in the face of temperature change and charge and discharge cycles. A model based on linear regressions, manufacturer characteristics, and integrating equations in the electrical model of electrochemical phenomena is proposed.

One of the main challenges in the efficient operation of a high-performance computing (HPC) center is the energy consumption generated by the operation of the data center where the HPC equipment is housed, mainly because this consumption is reflected in very high accounts payable, and this may affect the level of service offered to users. The study of the different factors and elements that can make energy consumption more efficient in these data centers provides an opportunity to focus these resources on elements that favor the use of HPC. The design variables provided by manufacturers to manage HPC systems and monitoring systems provide an accurate view of the behavior of these variables according to how they are used. HPC architectures are configured in a very particular way for each HPC data center, creating particular scenarios of operation and performance in each implementation. Various proposals and technologies have been developed for the analysis of the energy consumption of a data center, and the processing elements include a series of indicators and technologies that manufacturers have developed to determine the energy efficiency. This article seeks to identify this series of processing and performance variables, which affect the energy consumption of HPC equipment, for the implemented computing architectures based on the analysis of performance models to obtain a general over-view of their effect on energy consumption in a case study to identify the behaviors of particular job assignment factors and provide an analysis of the energy consumption under particular conditions.