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Partition-wise models offer a flexible approach for modeling complex and multidimensional data and are capable of producing interpretable results. They are based on partitioning the observed data into regions, each of which is modeled with a simple submodel. The success of this approach highly depends on the quality of the partition, as too large a region could lead to a nonsimple submodel, while...
Modern predictive modeling techniques are commonly used for modeling a target of interest based on a list of input variables. In general, these techniques are capable of identifying input variables associated with the target, but not for the purpose of identifying the causation relationship between target and inputs due to the fact that the data are observational data. Advanced technology has made...
Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This...
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