• Energy Research

[EN] Many governments are pushing for cleaner transportation. In particular, public transportation allows massive transportation of passengers, but it remains highly polluting, especially in high elevation cities. Thus, the progressive introduction of electric buses (EBS) will allow mitigating these environmental concerns. However, some technological problems must be addressed considering the massive penetration of EBs. The lack of flexibility and the time connection of scheduling for public transit make EBS harder than internal combustion ones. This work studies the impact of charging EBs at the bus station and suggests a new way to take EB aggregators into account to reduce energy costs while fulfilling grid restrictions. In addition, to find a different number of charging spots to be installed, a scheduling analysis is conducted. This works belongs to the projects IEA.JCG.20.01 and SIS.MGR.21.01 from Universidad de las Americas - Ecuador. The authors would like to thank Paulo Guerra-Teran from Universidad de las Americas - Ecuador for the fruitful discussion.

Biosphere–atmosphere interactions are a critical component of the Earth’s climate system. Many of these interactions are currently contributing to temperature increases and accelerating global warming. One of the main factors responsible for this is land use and land cover changes; in particular, this work models the interaction between Amazon rainforest deforestation and global temperatures. A Long Short-Term Memory (LSTM) neural network is proposed to forecast temperature trends, including mean, average minimum, and average maximum temperatures, in 20 major cities worldwide. The Amazon rainforest, often referred to as the Earth’s “lungs”, plays a pivotal role in regulating global climate patterns. Over the past two decades, this region has experienced significant deforestation, largely due to human activities. We hypothesize that the extent of deforestation in the Amazon can serve as a valuable proxy for understanding and predicting temperature changes in distant urban centers. Using a dataset that tracks cumulative deforestation from 2001 to 2021 across 297 municipalities in the Amazon rainforest, a multivariate time series model was developed to forecast temperature trends worldwide up to 2030. The input data reveal a variety of behaviors, including complex deforestation patterns. Similarly, the forecasted temperature data showcases diverse trends. While some cities are expected to exhibit a steady temperature increase, others may experience gradual changes, while some cities may undergo drastic and rapid temperature shifts. Our findings contribute to a deeper understanding of the far-reaching impacts of deforestation on global climate patterns and underscore the importance of preserving vital ecosystems like the Amazon rainforest.

This work models the Corporate Sustainability General Reporting Initiative (GRI) using a ternary attractor network. A dataset of years evolution of the GRI reports for a world-wide set of companies was compiled from a recent work and adapted to match the pattern coding for a ternary attractor network. We compare the performance of the network with a classical binary attractor network. Two types of criteria were used for encoding the ternary network, i.e., a simple and weighted threshold, and the performance retrieval was better for the latter, highlighting the importance of the real patterns’ transformation to the three-state coding. The network exceeds the retrieval performance of the binary network for the chosen correlated patterns (GRI). Finally, the ternary network was proved to be robust to retrieve the GRI patterns with initial noise. This work has been supported by Spanish grants MINECO (http://www.mineco.gob.es/) TIN2014-54580-R, TIN2017-84452-R, and by UAMSantander CEAL-AL/2017-08, and UDLA-SIS.MG.17.02. International Conference on Mining Intelligence and Knowledge Exploration. Cluj-Napoca, Romania, December 20–22, 2018

The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.

Se han dedicado varios esfuerzos al desarrollo de ciudades sostenibles para abordar los desafíos ambientales globales y el crecimiento de las áreas urbanas. En particular, el transporte tiene diversos problemas como la contaminación del aire, el ruido y el tráfico, que deben abordarse mediante la recopilación de información significativa sobre el tráfico y las condiciones de las carreteras de las ciudades. Automatizar el proceso de extracción de datos y la construcción de la red de calles permitirá construir modelos más útiles para estudiar el comportamiento del tráfico. Este trabajo presenta un enfoque de modelado de redes para identificar puntos de interés (extremos e internos) de la ciudad, a través de un recorte de bordes en el que el ganador se lleva todo y mapea el flujo de tráfico entre ellos. Dichos puntos pueden considerarse como entradas de una matriz Origen-Destino, donde dicha información puede utilizarse para modelar el comportamiento del tráfico entre puntos de interés. Se considera el estudio de caso de Quito, Ecuador. Además, para abordar los problemas ambientales, este documento fomenta la sustitución de los taxis de combustión interna por vehículos eléctricos. A partir de la comprensión del comportamiento del tráfico de vehículos, se llevó a cabo un emplazamiento de prefactibilidad de las estaciones de carga de taxis eléctricos (ET). Los resultados permitirán realizar el dimensionamiento de cada estación de carga considerando las restricciones de la red de energía eléctrica. Este trabajo tiene como objetivo garantizar un sistema de transporte sostenible basado en esta información crucial. Plusieurs efforts ont été consacrés au développement de villes durables pour relever les défis environnementaux mondiaux et la croissance des zones urbaines. En particulier, les transports ont divers problèmes tels que la pollution de l'air, le bruit et la circulation, qui doivent être résolus en collectant des informations importantes sur la circulation et les conditions routières des villes. L'automatisation du processus d'extraction des données et de la construction du réseau routier permettra de construire des modèles plus utiles pour étudier le comportement du trafic. Ce travail présente une approche de modélisation de réseau pour identifier les points d'intérêt (extrêmes et internes) de la ville, à travers un découpage gagnant-tout, et cartographier le flux de trafic entre eux. Ces points peuvent être considérés comme des entrées d'une matrice origine-destination, où ces informations peuvent être utilisées pour modéliser le comportement du trafic entre les points d'intérêt. L'étude de cas de Quito, en Équateur, est considérée. En outre, pour aborder les questions environnementales, ce document encourage le remplacement des taxis à combustion interne par des véhicules électriques. À partir de la compréhension du comportement de la circulation des véhicules, un emplacement de préfaisabilité des stations de recharge de taxi électrique (ET) a été effectué. Les résultats permettront d'effectuer le dimensionnement de chaque borne en tenant compte des contraintes du réseau électrique. Ce travail vise à assurer un système de transport durable basé sur ces informations cruciales. Several efforts have been devoted to developing sustainable cities to address global environmental challenges and the growth of urban areas. In particular, transportation has various issues such as air pollution, noise, and traffic, which have to be addressed by collecting significant information of the traffic and road conditions of the cities. Automating the data extraction process and street network construction will allow building more useful models to study traffic behavior. This work presents a network modeling approach to identify interest points (extreme and internal) of the city, through a winner-takes-all edge trimming, and mapping the traffic flow between them. Such points can be considered as entries of an Origin-Destination matrix, where such information can be used to model traffic behavior between interest points. The case study of Quito, Ecuador is considered. Besides, to address environmental issues, this paper encourages the replacement of internal combustion taxis with electric vehicles. From the understanding of the vehicle traffic behavior, a pre-feasibility siting of electric taxi (ET) charging stations was carried out. The results will allow performing the sizing of each charging station considering electric power network constraints. This work aims to ensure a sustainable transportation system based on this crucial information. تم تكريس العديد من الجهود لتطوير مدن مستدامة لمواجهة التحديات البيئية العالمية ونمو المناطق الحضرية. على وجه الخصوص، للنقل قضايا مختلفة مثل تلوث الهواء والضوضاء وحركة المرور، والتي يجب معالجتها من خلال جمع معلومات مهمة عن حركة المرور وظروف الطرق في المدن. ستسمح أتمتة عملية استخراج البيانات وبناء شبكة الشوارع ببناء نماذج أكثر فائدة لدراسة سلوك حركة المرور. يقدم هذا العمل نهج نمذجة الشبكة لتحديد نقاط الاهتمام (المتطرفة والداخلية) للمدينة، من خلال تقليم حافة الفائز يأخذ كل شيء، وتخطيط تدفق حركة المرور بينهما. يمكن اعتبار هذه النقاط كمدخلات لمصفوفة المنشأ والوجهة، حيث يمكن استخدام هذه المعلومات لنمذجة سلوك حركة المرور بين نقاط الاهتمام. يتم النظر في دراسة حالة كيتو، الإكوادور. إلى جانب ذلك، لمعالجة القضايا البيئية، تشجع هذه الورقة على استبدال سيارات الأجرة التي تعمل بالاحتراق الداخلي بالمركبات الكهربائية. من فهم سلوك حركة مرور المركبات، تم تنفيذ تحديد موقع ما قبل الجدوى لمحطات شحن سيارات الأجرة الكهربائية (ET). ستسمح النتائج بإجراء تحجيم كل محطة شحن مع مراعاة قيود شبكة الطاقة الكهربائية. يهدف هذا العمل إلى ضمان نظام نقل مستدام يعتمد على هذه المعلومات الهامة.

Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (<10 µg/m3) versus high (>25 µg/m3) and low (<10 µg/m3) versus moderate (10–25 µg/m3) concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data.

[EN] The integration of solar energy into power systems is essential for the future sustainability of power systems, particularly for isolated systems, such as microgrids, where establishing a primary transmission network is difficult. Therefore, the development of prediction methods becomes crucial to enable accurate forecasting of solar energy generation, facilitating efficient planning and operation of these systems and ensuring their long-term viability. This study proposes distinct forecasting models for solar irradiance forecasting: an autoregressive (AR) model, a Random Forest model, and a Long Short-Term Memory (LSTM) neural network. The methodology involves preprocessing the historical solar irradiance data and performing feature engineering to extract relevant input features. The architectural design, hyperparameter tuning, and training procedures of each model are discussed in detail. The findings indicate that the LSTM model exhibits enhanced performance compared to the AR model, while maintaining similar predictive accuracy to the Random Forest model in forecasting global solar irradiance. Both models yield a mean absolute percentage error of roughly 25%, with the LSTM exhibiting the lower error rate. Moreover, the LSTM model showcases an advancement over the AR model, resulting in a reduction of approximately 10 W/m2 for both root mean square error and mean absolute error. This finding highlights the effectiveness of LSTM networks in capturing long-term dependencies for accurate solar irradiance forecasting. Furthermore, an analysis of the models' interpretability is conducted, offering valuable insights into the key factors that contribute to the shaping of solar irradiance patterns. These insights hold practical significance for the optimization of renewable energy systems. This work has been funded by Universidad de las Americas, Ecuador, projects SIS.MGR.23.13.01 and IEA.JCG.20.01.