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Paper: Growing Gaussian Mixture Regression for Modeling Passive Chilled Beam Systems in Buildings – Wang et al.

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Primary software used Python
Software version 1.0
Course Computational Intelligence for Integrated Design
Primary subject AI & ML
Secondary subject Machine Learning
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Paper: Growing Gaussian Mixture Regression for Modeling Passive Chilled Beam Systems in Buildings – Wang et al. 0/0

Paper: Growing Gaussian Mixture Regression for Modeling Passive Chilled Beam Systems in Buildings – Wang et al. link copied

The following paper was published in a journal and may require a subscription to access. We do our best to reference open source papers but this is not always possible.

Authors: Liping Wang , James Braun , Sujit Dahal   

The researchers used an evolving learning approach called growing Gaussian mixture regression (GGMR) to estimate cooling rates in passive chilled beam (PCB) systems. The method entailed using actual system measurements and data from building energy models for training, evolution, and validation. To handle differences in system functioning that extend beyond the original training data, GGMR constantly modifies important parameters such as weight coefficients, means, and covariance matrices of Gaussian components. 

The study made a strong argument for GGMR’s efficacy as an evolving learning-based, data-driven strategy for precisely projecting cooling rates in PCB systems. The research also delves into the selection of crucial performance characteristics for GGMR models, such as the number of components, training data size, and learning rate.

APA: Wang, L., Braun, J., Dahal, S. (2022). An Evolving Learning Method -Growing Gaussian Mixture Regression- for Modeling Passive Chilled Beam Systems in Buildings. Energy and Buildings, Volume 268, 112227. https://www.sciencedirect.com/science/article/pii/S037877882200398X.

Software & Plug-Ins Used

  • EnergyPlus for model simulations 

  • Niagara/AX software for Living Lab simulations 

Paper Information

  • Title: An Evolving Learning Method -Growing Gaussian Mixture Regression- for Modeling Passive Chilled Beam Systems in Buildings 
  • Author(s): Liping Wang , James Braun, Sujit Dahal 
  • Year: 2022
  • Link: https://www.sciencedirect.com/science/article/pii/S037877882200398X
  • Type: Journal Paper
  • ML Tags: Gaussian Mixtures, Growing Gaussian Mixture Regression (GGMR)
  • Topic Tags: Operational Energy / Building Energy control
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