Abstract
Worldwide, human activities have a major impact on energy production and consumption. Urban areas, where the majority of the world population lives, are confronted with many environmental problems especially in emerging countries where a potential ecological transition is shadowed by frenetic economic development. At the same time, the deployment of intelligent infrastructures (Smart Grids), new technologies or paradigms (ubiquitous computing, Big Data) impact behaviors and practices of inhabitants of these areas. The ability to aggregate and model these digital traces in multiple dimensions could allow people to better understand their daily activities and promote more sustainable behaviors and practices by reducing the footprints related to every human collective activity. This paper aims to explore how to facilitate the decision-making process for inhabitants of these intelligent urban areas about their sustainable practices and lifestyles based on massive heterogeneous data in order to optimize the daily production and consumption of energy and meet the challenges of energy access and ecological transition.
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