Conference Paper

Spatio-temporal mapping of local areas for engaging communities in the planning of smart local energy initiatives


Community engagement in the planning and delivery of smart local energy initiatives is essential for their long-term success. Spatial and temporal visualisation of local energy flows can be used to engage communities in a more joined-up way. This paper describes the development and trial of an online and interactive smart local area energy mapping (LEMAP) tool for planning smart local energy neighbourhoods in Oxfordshire (UK). The spatial-temporal tool has been designed for community groups and residents. The LEMAP tool brings together public, private and crowdsourced data on energy demand, energy resources, building attributes, socio-demographics, fuel poverty and electricity networks within the ESRI ArcGIS platform. Postcode and dwelling level energy demand profiles are generated using the CREST energy demand model. The tool has been organised around three technical and three engagement elements that include ‘baselining’ local area energy flows in relation to socio-economic characteristics; ‘targeting’ suitable properties for low carbon technologies (LCT) such as rooftop solar, heat pumps, EV chargers; and ‘forecasting’ energy demand profiles at postcode level for different LCT scenarios. The engagement elements include: ‘Participatory mapping’ to allow residents to visualise their energy demand profiles, compare against the neighbourhood and see how the profile changes with LCTs; ‘Storymap’ for creating blogs on local energy flows; and ‘Forum’ to enable chats amongst users of LEMAP and project stakeholders. The LEMAP tool was applied to a socially-deprived but datarich neighbourhood in Oxford omprising over 2,500 households. A social enterprise organisation in Oxfordshire was trained online to use LEMAP to plan for energy management at neighbourhood level. Participatory mapping was found to enrich the tool and engage communities to provide local data through online surveys and highlight any discrepancies in the public and private data through local data interpretation.

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Gupta, Rajat
Jimenez-Moreno, Pablo
Donastorg Sosa, Angelines
Devine-Wright, Patrick

Oxford Brookes departments

School of Architecture


Year of publication: 2021
Date of RADAR deposit: 2022-02-09

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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