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Strategic Selection of Charging Stations for Electric Vehicles: A Data-Driven Approach in Lisbon District
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| 1.92 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
The rapid growth of electric vehicles (EVs) presents new challenges and opportunities for
urban infrastructure, particularly in the development of a robust charging network. This thesis
explores the current landscape of EV charging facilities in the Lisbon district of Portugal and
proposes a strategic framework for their expansion. Utilizing data analytics methods, including
K-means clustering, the study identifies high-demand areas lacking sufficient charging points
and suggests optimal locations for new installations. Key findings underscore the necessity of
an integrated approach involving policymakers, automakers, and community stakeholders. It
is also highlighting the beneficial impact of data-driven planning on the successful integration
of EVs into urban transport systems. The proposed solutions aim to not only fill the gaps in
the existing infrastructure but also to stimulate the transition to eco-friendly mobility, with
Lisbon serving as a case study for cities worldwide.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Palavras-chave
Electric vehicles charging station charging infrastructure data analytics K-means clustering SDG 7 - Affordable and clean energy SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 13 - Climate action