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Decision Support for Group-based Electricity Prices in Smart Grids

2016 | Americas Conference on Information Systems | Citations: 0
Authors: Stein, Nikolai; Flath, Christoph

Abstract: Dynamic electricity pricing is considered a primary lever for improving supply-d ... Expand

Abstract: Dynamic electricity pricing is considered a primary lever for improving supply-demand coordination in future smart energy markets. Yet, customer acceptance of complex pricing schemes is limited which prompts utility companies to focus on simpler time-of-use rate designs. Muratori and Rizzoni (2015) show that a combination of time-of-use rates with customer segmentation approaches can realize highly cost-efficient grid operations. We apply design-oriented research approach to develop an artifact which facilitates integrated customer segmentation and tariff design decisions while incorporating strategic customer behavior. We evaluate the artifact using empirical data and find that while these tariffs are indeed efficient from a societal perspective, it is not profitable for suppliers to offer them. Based on these findings we propose future research avenues to reconcile supplier strategies with customer expectations to pave the way for a more sustainable power system. Collapse

Semantic filters: k-medoids clustering

Topics: price management energy information system Python accounting system efficiency

Methods: design science design artifact k-means clustering cluster analysis mixed-integer programming
A personalized route recommendation service for theme parks using RFID information and tourist behavior
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2012 | Decision Support Systems | Citations: 9
Authors: Tsai, Chieh-Yuan; Chung, Shang-Hsuan

Abstract: Like any other industry, theme parks are now facing severe challenges from other ... Expand

Abstract: Like any other industry, theme parks are now facing severe challenges from other entertainment competitors. To survive in a rapidly changing environment, creating high quality products/services in terms of consumer preference has become a critical issue for theme park managers. To fulfill these needs, this paper develops a route recommendation system that supplies theme park tourists with the facilities they should visit and in what order. In the proposed system, tourist behaviors (i.e. visiting sequences and corresponding timestamps) are persistently collected through a Radio-Frequency Identification (RFID) system and stored in a route database. The database is then segmented into sub-groups based on the similarity among tourists’ visiting sequences and time lengths. Whenever a visitor requests a route recommendation service, the system identifies the sub-group most similar to that visitor's personal preferences and intended visitation time. Based on the retrieved visiting behavior data and current facility queuing situation identified by the RFID system, the proposed system generates a proper route suggestion for the visitor. A simulation case is implemented to show the feasibility of the proposed system. Based on the experimental results, it is clear that the recommended route satisfies visitor requirements using previous tourists’ favorite experiences. Collapse

Semantic filters: k-medoids clustering

Topics: database system radio frequency identification recommender system mobile application content-based filtering

Methods: computational algorithm cluster analysis experiment k-medoids clustering parametric test

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Decision Support for Group-based Electricity Prices in Smart Grids

A personalized route recommendation service for theme parks using RFID information and tourist behavior