Open Collections

Hierachical modeling of systems with similar components

International Conference on Applications of Statistics and Probability (12th : 2015 : Vancouver, B.C.)

Identifying optimal management policies for systems made up by similar components is a challenging task, due to dependence in the components’ behavior. In this setting, observations collected on one component are also relevant for learning the behavior of others. Probabilistic graphical models allow for consistent inference using all available data, taking dependence among components into account, while optimizing system operation. In this paper we propose a framework for management of systems made by similar components based on hierarchical Bayesian modeling, called Multiple Uncertain Partially Observable Markov Decision Processes (MU-POMDP), that overcomes some limitations of a previously proposed approaches. We describe a detailed numerical algorithm to learn the system parameters within this framework and we investigate its performance with an example of management of a wind farm (i.e., the system) made up by turbines of the same type (i.e., the components).

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This collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.

Vancouver : University of British Columbia Library

10.14288/1.0076244

Non UBC

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/