Nghiem, Truong X

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Now showing 1 - 2 of 2
  • Publication
    DR-Advisor: A Data Driven Demand Response Recommender System
    (2015-09-01) Behl, Madhur; Nghiem, Truong X; Mangharam, Rahul
    A data-driven method for demand response baselining and strategy evaluation is presented. Using meter and weather data along with set-point schedule information, we use an ensemble of regression trees to learn non-parametric data-driven models for predicting the power consumption of the building. This model can be used for evaluating demand response strategies in real-time, without having to learn complex models of the building. The methods have been integrated in an open-source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager by advising on which control actions should be during a demand response event. We provide a case study using data from a large commercial vistural test-bed building to evaluate the performance of the DR-Advisor tool. Keywords: demand response, regression trees, machine learning
  • Publication
    Campus-Wide Integrated Building Energy Simulation
    (2015-12-01) Behl, Madhur; Nghiem, Truong X; Bernal, Willy; Mangharam, Rahul
    Effective energy management for large campus facilities is becoming increasingly complex as modern heating and cooling systems comprise of several hundred subsystems interconnected to each other. Building energy simulators like EnergyPlus are exceedingly good at modeling a single building equipped with a standalone HVAC equipment. However, the ability to simulate a large campus and to control the dynamics and interactions of the subsystems is limited or missing altogether. In this paper, we use the Matlab-EnergyPlus MLE+ tool we developed, to extend the capability of EnergyPlus to co-simulate a campus with multiple buildings connected to a chilled water distribution to a central chiller plant with control systems in Matlab. We present the details of how this simulation can be set-up and implemented using MLE+'s Matlab/Simulink block. We utilize the virtual campus test-bed to evaluate the performance of several demand response strategies. We also describe a coordinated demand response scheme which can lead to load curtailment during a demand response event while minimizing thermal discomfort.