Hierachical modeling of systems with similar components Memarzadeh, Milad; Pozzi, Matteo; Kolter, J. Zico
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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