City 5.0: Citizen involvement in the design of future cities.

A citizen-centric view is key to channeling technological affordances into the development of future cities in which improvements are made with the quality of citizens' life in mind. This paper proposes City 5.0 as a new citizen-centric design paradigm for future cities, in which cities can be seen as markets connecting service providers with citizens as consumers. City 5.0 is dedicated to eliminating restrictions that citizens face when utilizing city services. Our design paradigm focuses on smart consumption and extends the technology-centric concept of smart city with a stronger view on citizens' roadblocks to service usage. Through a series of design workshops, we conceptualized the City 5.0 paradigm and formalized it in a semi-formal model. The applicability of the model is demonstrated using the case of a telemedical service offered by a Spanish public healthcare service provider. The usefulness of the model is validated by qualitative interviews with public organizations involved in the development of technology-based city solutions. Our contribution lies in the advancement of citizen-centric analysis and the development of city solutions for both academic and professional communities.

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions.

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.