A Methodology to Estimate High-Resolution Gridded Datasets on Energy Consumption Drivers in Ecuador’s Residential Sector during the 2010–2020 Period

Diego Moya (), César Arroba, Christian Castro, Cristian Pérez, Sara Giarola, Prasad Kaparaju, Ángel Pérez-Navarro and Adam Hawkes
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Diego Moya: Technology Outlook and Strategy, Technology Strategy and Planning Department, Saudi Aramco, Dhahran 34481, Saudi Arabia
César Arroba: Carrera de Ingeniería Mecánica, Facultad de Ingeniería Civil y Mecánica, Universidad Técnica de Ambato, Av. Los Chasquis y Río Payamino, Ambato 180207, Ecuador
Christian Castro: Carrera de Ingeniería Mecánica, Facultad de Ingeniería Civil y Mecánica, Universidad Técnica de Ambato, Av. Los Chasquis y Río Payamino, Ambato 180207, Ecuador
Cristian Pérez: Carrera de Ingeniería Mecánica, Facultad de Ingeniería Civil y Mecánica, Universidad Técnica de Ambato, Av. Los Chasquis y Río Payamino, Ambato 180207, Ecuador
Sara Giarola: Department of Chemical Engineering, Imperial College London, South Kensington, London SW7 2BX, UK
Prasad Kaparaju: School of Engineering & Built Environment, Griffith University, Brisbane, QLD 4111, Australia
Ángel Pérez-Navarro: Instituto de Ingeniería Energética, Universitat Politècnica de València, 46022 Valencia, Spain
Adam Hawkes: Department of Chemical Engineering, Imperial College London, South Kensington, London SW7 2BX, UK

Energies, 2023, vol. 16, issue 10, 1-16

Abstract: 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 km 2 ), 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.

Keywords: residential energy consumption; end-use energy; spatial analysis; spatiotemporal approach; geographical information system; gridded energy data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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