Publications
17 results found
Muñoz-Valverde P, Villena-López O, Mayorga-Ases L, et al., 2024, Prediction of abrasive wear and surface hardness of printed parts by SLA technology, Ingenius, Pages: 19-31, ISSN: 1390-650X
<jats:p>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.</jats:p>
Moya D, Arroba C, Castro C, et al., 2024, Long-Term Sustainable Energy Transition of Ecuador’s Residential Sector Using a National Survey, Geospatial Analysis with Machine Learning, and Agent-Based Modeling, Pages: 23-40, ISBN: 9783031521706
In 2020, Ecuador’s residential sector consumed 13 million Barrels of Oil Equivalent (BOE), representing 15.7% of the total energy consumption in urban and rural households in the country. Of this consumption, liquefied petroleum gas accounted for 51.8%, electricity 38.4%, firewood 9.7%, and natural gas 0.1%. The main uses in this sector are home heating (49%), home ventilation (29%), and water heating, cooking, lighting, and household appliances (22%). This research aims to study the long-term energy transition of Ecuador’s residential sector. The methodology applies the geoAI MUSE-RASA framework and is based on a national survey, geospatial analysis of large spatiotemporal datasets applying machine learning techniques, and agent-based modeling. The survey results and the spatial distribution of per capita GDP show that the population can be classified into five agents, characterized by investment objectives, search rules, decision strategies, and a budget for investing in household energy technologies. Additional results include national and agent-specific demand, supply, consumption, and emissions. The sustainable scenario shows that by 2050, the total energy demand in the residential sector will reach 103.2 PJ, distributed among home heating (45 PJ), water heating (19 PJ), space ventilation (0.2 PJ), cooking (12 PJ), lighting (5 PJ), and appliances (22 PJ). In the case of home heating, three technologies will play a significant role in the sector’s sustainable transition: electric boilers, biomass boilers, and heat pumps by 2050. The results of this research can be used for evaluating energy policy when considering the spatial distribution of the population and their socioeconomic and developmental characteristics.
Moya Pinta DA, Copara D, Olivo A, et al., 2023, MUSE-RASA captures human dimension in climate-energy-economic models via global geoAI-ML agent datasets, Scientific Data, Vol: 10, ISSN: 2052-4463
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.
Moya D, Arroba C, Castro C, et al., 2023, A Methodology to Estimate High-Resolution Gridded Datasets on Energy Consumption Drivers in Ecuador's Residential Sector during the 2010-2020 Period, ENERGIES, Vol: 16
Moya D, Copara D, Borja A, et al., 2022, Geospatial and temporal estimation of climatic, end-use demands, and socioeconomic drivers of energy consumption in the residential sector in Ecuador, Energy Conversion and Management, Vol: 261, Pages: 115629-115629, ISSN: 0196-8904
It is widely acknowledged that the drivers for energy consumption in the residential sector are ambient temperature, energy demand, population density, and socio-economic conditions. However, there are no studies in the literature assessing the temporal and spatial distribution of these drivers for a region or country. The decision-making process of the energy transition will be enhanced by using geospatial-resolved and temporal-explicit energy consumption drivers. This study estimates the climatic, end-use demands, and socio-economic drivers of energy consumption in the residential sector of Ecuador at a high spatio-temporal resolution between 2010 and 2020. This research uses publicly available datasets to calculate seven energy consumption drivers in the residential sector of Ecuador: (1) calibrated gridded population density at 1 km2 resolution; (2) validated gridded space heating demand at 1 km2 resolution; (3) validated gridded space cooling demand at 1 km2 resolution; (4) validated gridded water heating demand at 1 km2 resolution; (5) calibrated gridded gross domestic product at 1 km2 resolution; (6) calibrated gridded gross domestic product per capita at 1 km2 resolution; and (7) calibrated regional human development index, at city level. Disaggregation of the drivers at a high spatial resolution for the entire country in a range of 10 years was considered. The final high-1 km2 resolution results can be used for the evaluation of different energy policies in terms of long-term planning and in techno-economic modelling of energy systems and decarbonisation pathways.
Moya D, Copara D, Amores J, et al., 2022, Characterization of energy consumption agents in the residential sector of Ecuador based on a national survey and geographic information systems for modelling energy systems, ENFOQUE UTE, Vol: 13, Pages: 68-97, ISSN: 1390-9363
Moya D, Giarola S, Hawkes A, 2022, Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide, 2021 IEEE International Conference on Big Data (Big Data), Publisher: IEEE, Pages: 4035-4046
Geospatial big data analytics has received much attention in recent years for the assessment of energy data. Globally, spatial datasets relevant to the energy field are growing rapidly every year. This research has analysed large gridded datasets of outdoor temperature, end-use energy demand, end-use energy density, population and Gros Domestic Product to end with usable inputs for energy models. These measures have been recognised as a means of informing infrastructure investment decisions with a view to reaching sustainable transition of the residential sector. However, existing assessments are currently limited by a lack of data clarifying the spatio-temporal variations within end-use energy demand. This paper presents a novel Geographical Information Systems (GIS)-based methodology that uses existing GIS data to spatially and temporally assess the global energy demands in the residential sector with an emphasis on space heating. Here, we have implemented an Unsupervised Machine Learning (UML)-based approach to assess large raster datasets of 165 countries, covering 99.6% of worldwide energy users. The UML approach defines lower and upper limits (thresholds) for each raster by applying GIS-based clustering techniques. This is done by binning global high-resolution maps into re-classified raster data according to the same characteristics defined by the thresholds to estimate intranational zones with a range of attributes. The spatial attributes arise from the spatial intersection of re-classified layers. In the new zones, the energy demand is estimated, so-called energy demand zones (EDZs), capturing complexity and heterogeneity of the residential sector. EDZs are then used in energy systems modelling to assess a sustainable scenario for the long-term transition of space heating technology and it is compared with a reference scenario. This long-term heating transition is spatially resolved in zones with a range of spatial characteristics to enhance the assessment
Akinsipe OC, Moya D, Kaparaju P, 2021, Design and economic analysis of off-grid solar PV system in Jos-Nigeria, JOURNAL OF CLEANER PRODUCTION, Vol: 287, ISSN: 0959-6526
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- Citations: 29
Moya D, Budinis S, Giarola S, et al., 2020, Agent-based scenarios comparison for assessing fuel-switching investment in long-term energy transitions of the India’s industry sector, Applied Energy, Vol: 274, Pages: 1-26, ISSN: 0306-2619
This paper presents the formulation and application of a novel agent-based integrated assessment approach to model the attributes, objectives and decision-making process of investors in a long-term energy transition in India’s iron and steel sector. It takes empirical data from an on-site survey of 108 operating plants in Maharashtra to formulate objectives and decision-making metrics for the agent-based model and simulates possible future portfolio mixes. The studied decision drivers were capital costs, operating costs (including fuel consumption), a combination of capital and operating costs, and net present value. Where investors used a weighted combination of capital cost and operating costs, a natural gas uptake of ~12PJ was obtained and the highest cumulative emissions reduction was obtained, 2 Mt CO2 in the period from 2020 to 2050. Conversely if net present value alone is used, cumulative emissions reduction in the same period was lower, 1.6 Mt CO2, and the cumulative uptake of natural gas was equal to 15PJ. Results show how the differing upfront investment cost of the technology options could cause prevalence of high-carbon fuels, particularly heavy fuel oil, in the final mix. Results also represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. The perception of high capital expenditures for decarbonisation represents a significant barrier to the energy transition in industry and should be addressed via effective policy making (e.g. carbon policy/price).
Sachs J, Moya D, Giarola S, et al., 2019, Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector, Applied Energy, Vol: 250, Pages: 48-62, ISSN: 0306-2619
Climatic conditions, population density, geography, and settlement structure all have a strong influence on the heating and cooling demand of a country, and thus on resulting energy use and greenhouse gas emissions. In particular, the choice of heating or cooling system is influenced by available energy distribution infrastructure, where the cost of such infrastructure is strongly related to the spatial density of the demand. As such, a better estimation of the spatial and temporal distribution of demand is desirable to enhance the accuracy of technology assessment. This paper presents a Geographical Information System methodology combining the hourly NASA MERRA-2 global temperature dataset with spatially resolved population data and national energy balances to determine global high-resolution heat and cooling energy density maps. A set of energy density bands is then produced for each country using K-means clustering. Finally, demand profiles representing diurnal and seasonal variations in each band are derived to capture the temporal variability. The resulting dataset for 165 countries, published alongside this article, is designed to be integrated into a new integrated assessment model called MUSE (ModUlar energy systems Simulation Environment)but can be used in any national heat or cooling technology analysis. These demand profiles are key inputs for energy planning as they describe demand density and its fluctuations via a consistent method for every country where data is available.
Moya D, Aldas C, Kaparaju P, 2018, Geothermal energy: Power plant technology and direct heat applications, RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 94, Pages: 889-901, ISSN: 1364-0321
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- Citations: 149
Moya D, Paredes J, Kaparaju P, 2018, Technical, financial, economic and environmental pre-feasibility study of geothermal power plants by RETScreen-Ecuador's case study, RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 92, Pages: 628-637, ISSN: 1364-0321
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- Citations: 22
Moya D, Paredes J, Kaparaju P, 2018, Method for the technical, financial, economic and environmental pre-feasibility study of geothermal power plants by RETScreen - Ecuador's case study, METHODSX, Vol: 5, Pages: 524-531, ISSN: 2215-0161
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- Citations: 6
Hidalgo A, Villacres L, Hechavarria R, et al., 2017, Proposed integration of a photovoltaic solar energy system and energy efficient technologies in the lighting system of the UTA-Ecuador, 9th International Conference on Sustainability and Energy in Buildings (SEB), Publisher: ELSEVIER SCIENCE BV, Pages: 296-305, ISSN: 1876-6102
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- Citations: 5
Moya D, Aldas C, Lopez G, et al., 2017, Municipal solid waste as a valuable renewable energy resource: a worldwide opportunity of energy recovery by using Waste-To-Energy Technologies, 9th International Conference on Sustainability and Energy in Buildings (SEB), Publisher: ELSEVIER SCIENCE BV, Pages: 286-295, ISSN: 1876-6102
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- Citations: 146
Moya D, Aldas C, Jaramillo D, et al., 2017, Waste-To-Energy Technologies: an opportunity of energy recovery from Municipal Solid Waste, using Quito - Ecuador as case study, 9th International Conference on Sustainability and Energy in Buildings (SEB), Publisher: ELSEVIER SCIENCE BV, Pages: 327-336, ISSN: 1876-6102
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- Citations: 28
Moya D, Torres R, Stegen S, 2016, Analysis of the Ecuadorian energy audit practices: A review of energy efficiency promotion, RENEWABLE & SUSTAINABLE ENERGY REVIEWS, Vol: 62, Pages: 289-296, ISSN: 1364-0321
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- Citations: 21
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