Modern demand-side management techniques are an integral part of the envisioned smart grid paradigm. They require an active involvement of the consumer for an optimization of the grid's efficiency and a better utilization of renewable energy sources. This applies especially in so called demand dispatch systems, where consumers are required to proactively communicate their flexibilities. However, a monetary compensation may not sufficiently motivate the individual consumer for a sustainable participation in such a program. The proposed approach uses a motivational framework leveraging the novel area of gamification, which applies well-known game mechanics, such as points and leaderboards, to engage customers in the system. This is accomplished by embedding a special scoring system and social competition aspects into a stimulating user interface for the definition and management of flexible energy demand. In a first user study, the system showed a high user acceptance and the potential to engage consumers in participation.

The topic of managing uncertain data has been explored in many ways. Different methodologies for data storage and query processing have been proposed. As the availability of management systems grows, the research on analytics of uncertain data is gaining in importance. Similar to the challenges faced in the field of data management, algorithms for uncertain data mining also have a high performance degradation compared to their certain algorithms. To overcome the problem of performance degradation, the MCDB approach was developed for uncertain data management based on the possible world scenario. As this methodology shows significant performance and scalability enhancement, we adopt this method for the field of mining on uncertain data. In this paper, we introduce a clustering methodology for uncertain data and illustrate current issues with this approach within the field of clustering uncertain data.

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