Costas J. Spanos and Zhaoyi Kang

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2015-217

December 1, 2015

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-217.pdf

In this work, modeling and monitoring of end-use power consumption in commercial buildings are investigated through both Top-Down and Bottom-Up approaches. In the Top-Down approach, an adaptive support vector regression (ASVR) model is developed to accommodate the nonlinearity and nonstationarity of the macro-level time series, thus providing a framework for the modeling and diagnosis of end-use power consumption. In the Bottom-Up approach, an appliance-data-driven stochastic model is built to predict each end-use sector of a commercial building. Power disaggregation is studied as a technique to facilitate Bottom-Up prediction. In Bottom-Up monitoring and diagnostic detection, a new dimensionality reduction technique is explored to facilitate the analysis of multivariate binary behavioral signals in building end-uses.

Advisors: Costas J. Spanos

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BibTeX citation:

@phdthesis{Spanos:EECS-2015-217,
    Author= {Spanos, Costas J. and Kang, Zhaoyi},
    Title= {Efficient Multi-Level Modeling and Monitoring of End-use Energy Profile in Commercial Buildings},
    School= {EECS Department, University of California, Berkeley},
    Year= {2015},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-217.html},
    Number= {UCB/EECS-2015-217},
    Abstract= {In this work, modeling and monitoring of end-use power consumption in commercial buildings are investigated through both Top-Down and Bottom-Up approaches. In the Top-Down approach, an adaptive support vector regression (ASVR) model is developed to accommodate the nonlinearity and nonstationarity of the macro-level time series, thus providing a framework for the modeling and diagnosis of end-use power consumption. In the Bottom-Up approach, an appliance-data-driven stochastic model is built to predict each end-use sector of a commercial building. Power disaggregation is studied as a technique to facilitate Bottom-Up prediction. In Bottom-Up monitoring and diagnostic detection, a new dimensionality reduction technique is explored to facilitate the analysis of multivariate binary behavioral signals in building end-uses.},
}

EndNote citation:

%0 Thesis
%A Spanos, Costas J. 
%A Kang, Zhaoyi 
%T Efficient Multi-Level Modeling and Monitoring of End-use Energy Profile in Commercial Buildings
%I EECS Department, University of California, Berkeley
%D 2015
%8 December 1
%@ UCB/EECS-2015-217
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-217.html
%F Spanos:EECS-2015-217