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Structure-Regularized Partition-Regression Models for Nonlinear System-Environment Interactions

Abstract Under different environmental conditions, the relationship between the design and operational variables of a system and the system’s performance is likely to vary and is difficult to be described by a single model. The environmental variables (e.g., temperature, humidity) are not controllable while the variables of the system (e.g. heating, cooling) are mostly controllable. This phenomenon has been widely seen in the areas of building energy management, mobile communication networks, and wind energy. To account for the complicated interaction between a system and the multivariate environment under which it operates, a Sparse Partitioned-Regression (SPR) model is proposed, which automatically searches for a partition of the environmental var... (more)
Created Date 2018
Contributor Ning, Shuluo (Author) / Li, Jing (Advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Rafi, Tanveer A (Committee member) / Arizona State University (Publisher)
Subject Industrial engineering
Type Doctoral Dissertation
Extent 96 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Industrial Engineering 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis