Diego Moya

Traditional models deliberately simplify millions of consumers into a single, homogeneous, representative agent with perfect market knowledge and rational expectations, limiting their capacity to capture real-world complexities. To address this limitation in mainstream models, this article provides global datasets to parametrise energy consumers within climate-energy-economy models considering climate-driven energy demand, socioeconomic and demographic factors. The datasets emerge from applying geospatial artificial intelligence, machine learning and big data analytics on a range of geospatial parameters at 1 km² resolution. Twenty distinctive energy consumers are defined using three heterogeneous geospatial features, eight diverse and two evolving parameters. This parametrisation of consumers strengthens the applicability of climate-energy-economy models to guide effective, equitable and just climate policy design. This comprehensive analysis of complex interactions between climate, socioeconomic and demographic factors supports more realistic decision-making for a sustainable transition reset. This research emphasises the geospatial distribution of energy consumers to enhance technoeconomic assessment, understanding consumer dynamics for consumer-led resource allocation and informed policy implementation. These datasets can be used in climate-energy-economy models to parametrise consumers beyond traditional approaches.

In the present study, a prediction of hardness deterioration and abrasive wear was performed through a neural network using artificial intelligence on a material printed in SLA. This article aims to predict the mechanical properties, wear resistance and surface hardness of parts manufactured by SLA stereolithography printing. A full factorial DOE was used to associate the peculiar parameters (print orientation, cure time, layer height) to perform experiments. The mechanical properties were evaluated according to ASTM regulations, with the objective of obtaining feeding data and validation of the predictions of the Taber Wear Index and hardness using an artificial neural network. The experimental results are in good agreement with the measured data with satisfactory prediction errors with a mean square error (MSE) of 0.01 corresponding to abrasive wear using the clear resin and a mean absolute error (MSE) of 0.09 with an R2 of 0.756, the prediction with the neural network with a mean square error (MSE) of 2.47 corresponding to abrasive wear using the tough resin and a mean absolute error (MSE) of 14.3 with an R2 of 0.97. It was shown that the accuracy of the prediction is reasonable, and the network has the potential to be improved if the experimental database for training the network could be expanded. Therefore, wear and hardness mechanical properties can be predicted appropriately with an ANN.

This article provides a combined geospatial artificial intelligence-machine learning, geoAI-ML, agent-based, data-driven, technology-rich, bottom-up approach and datasets for capturing the human dimension in climate-energy-economy models. Seven stages were required to conduct this study and build thirteen datasets to characterise and parametrise geospatial agents in 28 regions, globally. Fundamentally, the methodology starts collecting and handling data, ending with the application of the ModUlar energy system Simulation Environment (MUSE), ResidentiAl Spatially-resolved and temporal-explicit Agents (RASA) model. MUSE-RASA uses AI-ML-based geospatial big data analytics to define eight scenarios to explore long-term transition pathways towards net-zero emission targets by mid-century. The framework and datasets are key for climate-energy-economy models considering consumer behaviour and bounded rationality in more realistic decision-making processes beyond traditional approaches. This approach defines energy economic agents as heterogeneous and diverse entities that evolve in space and time, making decisions under exogenous constraints. This framework is based on the Theory of Bounded Rationality, the Theory of Real Competition, the theoretical foundations of agent-based modelling and the progress on the combination of GIS-ABM.

There are no methodologies in the literature for estimating the temporal and spatial distribution of consumption drivers for the residential sector of a region or country. Factors such as energy requirement, population density, outdoor temperature, and socioeconomic aspects are considered the major drivers of consumption and have been found to directly influence residential energy consumption. In this study, a methodology is proposed to evaluate the impact of the above drivers in domestic energy consumption in Ecuador between 2010 and 2020 using publicly available data. This methodology aims to provide a spatiotemporal approach to estimate high-resolution gridded datasets for a 10-year period, 2010–2020, assessing seven energy drivers: (1) gridded population density, (2) gridded space heating requirements, (3) gridded space cooling requirements, (4) gridded water heating requirements, (5) gridded Gross Domestic Product (GDP), (6) gridded per capita GDP, and (7) the Human Development Index (HDI). Drivers 1 to 6 were analyzed at one square kilometer (1 km²), whereas HDI was analyzed at the city level. These results can be used to evaluate energy-planning strategies in a range of sustainable scenarios. This methodology can be used to evaluate a range of consumption drivers to evaluate long-term energy policies to reach the net-zero target by midcentury. The proposed methodology can be reproduced in other countries and regions. Future research could explore the spatiotemporal correlation of the consumption drivers provided in this study.

Esta investigación presenta la implementación de un sistema web para gestionar las cadenas de suministros de alimentos de las asociaciones comunitarias y familiares de la Provincia de Tungurahua, Ecuador. La metodología está basada en las fases del modelo de desarrollo en cascada que permite recabar información de las necesidades de negocio más relevantes. Este incluye el diseño y desarrollo del producto de software para su posterior proceso de validación. Los resultados reflejan que la implementación de un sistema web configurado bajo Odoo ERP agilita los procesos de comercialización y mejora la trazabilidad de los productos de la zona de estudio. Se concluye que el sistema implementado responde a las necesidades del sector agrícola, apoya la creación de nuevos canales y mejora la gestión de los procesos de comercialización de productos de las cadenas alimentarias post COVID-19.

El sector residencial es un ejemplo de los retos que se presentan al momento de modelar la heterogeneidad del sistema energético en búsqueda de la evaluación de la política energética. Algunas de las características que influyen en la oferta y demanda de energía del sector residencial son el tipo de consumo, el ingreso de los hogares, la composición familiar, el tipo de vivienda, la zona climática de residencia, entre otras. El objetivo de esta investigación es caracterizar los agentes de consumo energético en el sector residencial del Ecuador. Para lograrlo, en este artículo se presenta una metodología que combina una encuesta nacional (en línea y puerta a puerta) con sistemas de información geográfica (SIG). La encuesta permite definir las principales motivaciones, objetivos de inversión y método de decisión de los agentes al momento de renovar o mantener tecnologías energéticas del hogar. Para esto, se ha clasificado a los agentes con base en su nivel de ingresos. Los SIG permiten localizar espacialmente a los agentes y tener una estimación sobre la población que pertenece a cada agente por el nivel de ingreso. Los resultados se presentan por nivel de ingreso para cada grupo de agentes y se geolocalizan caracterizándolos de forma espacial. Se determina los atributos cuantitativos de cada agente con el fin de ser utilizados en modelación de sistemas de energía.