Search

The rising digitisation of the energy system and related services is unveiling an enormous opportunity for energy stakeholders to leverage on Big Data & AI technologies for improved decision making and coping with challenges emerging from an increasingly complex and interconnected energy system. Initiatives in the field of Big Data Reference Architectures, like IDSA, GAIA-X or FIWARE provide generic frameworks to share, manage and process Big Data. Through alignment among them and integration of missing aspects, an interoperable and secure framework for the energy comes into view. The Reference Architecture presented in this paper moves towards this goal and will be instantiated in a set of concrete use cases within the European Energy Sector. Structurally inspired by SGAM and the BRIDGE Reference Architecture, it puts concrete analytics processes and data source components into context, taking important issues of Data Governance, Security, and Value Creation into account., The paper presented at the 13th International Conference on Information, Intelligence, Systems and Applications (IISA2022), which took place in Corfu. The work presented is based on research conducted within the framework of the H2020 European Commission project BD4NRG (Grant Agreement No. 872613). The content of the paper is the sole responsibility of its authors and does not necessary reflect the views of the EC.

Transportation and mobility in smart cities are undergoing a grave transformation as new ways of mobility are introduced to facilitate seamless traveling, addressing travelers’ needs in a personalized manner. A novel concept that has been recently introduced is Mobility-as-a-Service (MaaS), where mobility services are bundled in MaaS Plans and offered to end-users through a single digital platform. The present paper introduces a recommender system for MaaS Plans selection that supports travelers to select bundles of mobility services that fit their everyday transportation needs. The recommender filters out unsuitable plans and then ranks the remaining ones on the basis of their similarity to the users’ characteristics, habits and preferences. The recommendation approach is based on Constraint Satisfaction Problem (CSP) formalisms combined with cosine similarity techniques. The proposed method was evaluated in experimental settings and was further embedded in real-life pilot MaaS applications. The experimental results showed that the proposed approach provides lists of MaaS PlanMaaS Plans that users would choose in a real-life MaaS setting, in most of the cases. Moreover, the results of the real-life pilots showed that the majority of the participants chose an actual MaaS Plan from the top three places of the recommendation lists.

Rendering transport behaviours more sustainable is a pressing issue of our times. In this paper, we rely on the deep penetration of mobile phones in order to influence citizens’ behavior through data-driven mobility and persuasive profiles. Our proposed approach aims to nudge users on a personalized level in order to change their mobility behavior and make more sustainable choices. To achieve our goal, first we leverage pervasive mobile sensing to uncover users’ mobility patterns and use of transportation modes. Second, we construct users’ persuadability profiles by considering their personality and mobility behavior. With the use of the aforementioned information we generate personalized interventions that nudge users to adopt sustainable transportation habits. These interventions rely on persuasive technologies and are embedded in a route planning application for smartphones. A pilot study with 30 participants using the system for 6 weeks provided fairly positive evaluation results in terms of the acceptance of our approach and revealed instances of behavioural change.

Manufacturing operations can take substantial advantage of the proactivity concept by utilising event-driven information systems, able to process the sensor data and to provide proactive recommendations. Despite the recent advances in technology and information systems and the variety of methods for prognosis, decision models for joint maintenance and inventory optimization on the basis of real-time prognostic information have not been explored. We propose a proactive event-driven decision model for joint predictive maintenance and spare parts inventory optimization which addresses the Decide phase of the “Detect- Predict- Decide- Act” model and can be embedded to an Event Driven Architecture (EDA) for real-time processing in the frame of e-maintenance concept. The proposed approach was tested in a real manufacturing scenario in automotive lighting equipment industry and proved that maintenance and inventory costs can be significantly reduced by transforming the company's maintenance strategy from time-based to Condition Based Maintenance (CBM).

The mobility landscape has changed over the last years with the advent of innovative transportation solutions which are being constantly deployed whereas more are set to emerge in the near future such as autonomous vehicles and flying taxis. In this landscape a new mobility paradigm has come to the forefront during the last years through Mobility as a Service (MaaS). This refers to a seamless integration of various forms of transport services accessible through one single digital platform. The concept of MaaS is aligned with Smart mobility, one of main Smart City components which refers to smart manners of people and goods mobility through the integration of all transport modes, ICT solutions, zero pollution and congestions and so on [1]. A key enabler for MaaS is technology that can provide the means for complex mobility ecosystems to be integrated, while allowing diverse service providers and related stakeholders (e.g. payment management organizations) to perform transactions between each other (for booking, invoicing, payment, etc.) as well as interact with the traveler, acquire and exchange operational data, and process it into information that can be used to support MaaS related business processes. Such an ecosystem can be centrally coordinated when all participants in the value chain have the same understanding of what needs to be delivered and accept to follow a common approach, which can be proprietary, customized and aligned to vendor specific guidelines. As the MaaS ecosystem expands and the coordination of an increasing number of mobility solutions comes into play, the complexity can be such that new decentralized solutions are required. In this work in progress paper we discuss the role of blockchain technology for enabling seamless mobility for MaaS.

In this paper, we study the effectiveness of personalized persuasive interventions to change urban travelers’ mobility behavior and nudge them towards more sustainable transport choices. More specifically, we embed a set of persuasive design elements in a route planning application and investigate how they affect users’ travel choices. The design elements take into consideration the style, the intensity, the target of persuasive interventions as well as users’ characteristics and the trip purpose. Our results show evidence that our proposed approach motivates users on a personal level to change their mobility behavior and make more sustainable choices. Furthermore, by personalizing the persuasive interventions while considering combinations of interventions styles (in our case messages and visualizations) as well as adjusting the intensity of persuasive interventions according to the trip purpose and the transport modes of the routes which the user is nudged to follow, the effects of the persuasive interventions can be increased.