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Bottom-up assessment of household electricity consumption in dynamic cities of the Global South-Evidence from Kigali, Rwanda

Vetter-Gindele, Jannik and Bachofer, Felix and Braun, Andreas and Uwayezu, Ernest and Rwanyiziri, Gaspard and Eltrop, Ludger (2023) Bottom-up assessment of household electricity consumption in dynamic cities of the Global South-Evidence from Kigali, Rwanda. Frontiers in Sustainable Cities, 5, pp. 1-17. Frontiers Media S.A. doi: 10.3389/frsc.2023.1130758. ISSN 2624-9634.

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Data on electricity consumption is crucial for assessing and modeling energy systems, making it a key element of sustainable urban planning. However, many countries in the Global South struggle with a shortage of statistically valid, geocoded, and disaggregated household-level data. This paper aims to develop a generic methodology for the generation of such a database in terms of electricity consumption. The methodology was tested in Kigali, the capital city of Rwanda, with a focus on all single-family residential building types of the inner city. Discrete data on buildings is obtained through combined information products derived from very high resolution (VHR) satellite imagery, field surveys, and computer assisted personal interviewing. In total, 509 valid geocoded survey datasets were used to evaluate and model household electricity consumption, as well as electrical appliance ownership. The study's findings reveal that the arithmetic mean of specific electricity consumption was 3.66 kWh per household per day and 345 kWh per capita per year in 2015. By subdividing the data into distinct building types as well as their spatial location, and weighting the specific values according to their proportion in the study area, a more accurate mean value of 1.88 kWh per household per day and 160 kWh per capita per year was obtained. Applying this weighted mean to extrapolate household electricity consumption for the study area, in conjunction with the sample's precision level, resulted in an estimate of 126–137 GWh for the year 2015. In contrast, using the arithmetic mean would have led to values twice as high, even exceeding the total electricity consumption of the entire city, including multi-family and non-residential buildings. The study highlights the significance of on-site data collection combined with geospatial mapping techniques in enhancing of understanding of residential energy systems. Using building types as indicators to distinguish between households with contrasting electricity consumption and electrical appliance load levels can address the challenges posed by rapid urban growth in the Global South. This proposed method can assist municipal administrations in establishing a database that can be updated resource-efficiently at regular intervals by acquiring new satellite images.

Item URL in elib:https://elib.dlr.de/196680/
Document Type:Article
Title:Bottom-up assessment of household electricity consumption in dynamic cities of the Global South-Evidence from Kigali, Rwanda
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Vetter-Gindele, JannikIER Uni Stuttgarthttps://orcid.org/0000-0003-0802-2805UNSPECIFIED
Bachofer, FelixUNSPECIFIEDhttps://orcid.org/0000-0001-6181-0187UNSPECIFIED
Braun, AndreasUNSPECIFIEDhttps://orcid.org/0000-0001-8630-1389UNSPECIFIED
Uwayezu, ErnestUniversity of RwandaUNSPECIFIEDUNSPECIFIED
Rwanyiziri, GaspardUniversity of RwandaUNSPECIFIEDUNSPECIFIED
Eltrop, LudgerIER Uni Stuttgarthttps://orcid.org/0000-0002-1721-7607UNSPECIFIED
Date:15 August 2023
Journal or Publication Title:Frontiers in Sustainable Cities
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 1-17
Publisher:Frontiers Media S.A
Series Name:Sec. Urban Energy End-Use
Keywords:urban, energy, Earth observation, modelling
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Bachofer, Dr. Felix
Deposited On:18 Sep 2023 09:42
Last Modified:18 Sep 2023 09:42

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