Data Predictive Control for Peak Power Reduction
Achin Jain, Rahul Mangharam, Madhur Behl

Citation
Achin Jain, Rahul Mangharam, Madhur Behl. "Data Predictive Control for Peak Power Reduction". Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, ACM, 109-118, 16, November, 2016.

Abstract
Decisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs.

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  • HTML
    Achin Jain, Rahul Mangharam, Madhur Behl. <a
    href="http://www.terraswarm.org/pubs/902.html"
    >Data Predictive Control for Peak Power
    Reduction</a>, Proceedings of the 3rd ACM
    International Conference on Systems for Energy-Efficient
    Built Environments, ACM, 109-118, 16, November, 2016.
  • Plain text
    Achin Jain, Rahul Mangharam, Madhur Behl. "Data
    Predictive Control for Peak Power Reduction".
    Proceedings of the 3rd ACM International Conference on
    Systems for Energy-Efficient Built Environments, ACM,
    109-118, 16, November, 2016.
  • BibTeX
    @inproceedings{JainMangharamBehl16_DataPredictiveControlForPeakPowerReduction,
        author = {Achin Jain and Rahul Mangharam and Madhur Behl},
        title = {Data Predictive Control for Peak Power Reduction},
        booktitle = {Proceedings of the 3rd ACM International
                  Conference on Systems for Energy-Efficient Built
                  Environments},
        organization = {ACM},
        pages = {109-118},
        day = {16},
        month = {November},
        year = {2016},
        abstract = {Decisions on how best to optimize today's energy
                  systems operations are becoming ever so complex
                  and conflicting such that model-based predictive
                  control algorithms must play a key role. However,
                  learning dynamical models of energy consuming
                  systems such as buildings, using grey/white box
                  approaches is very cost and time prohibitive due
                  to its complexity. This paper presents data-driven
                  methods for making control-oriented model for peak
                  power reduction in buildings. Specifically, a data
                  predictive control with regression trees (DPCRT)
                  algorithm, is presented. DPCRT is a finite
                  receding horizon method, using which the building
                  operator can optimally trade off peak power
                  reduction against thermal comfort without having
                  to learn white/grey box models of the systems
                  dynamics. We evaluate the performance of our
                  method using a DoE commercial reference virtual
                  test-bed and show how it can be used for learning
                  predictive models with 90% accuracy, and for
                  achieving 8.6% reduction in peak power and costs.},
        URL = {http://terraswarm.org/pubs/902.html}
    }
    

Posted by Mary Stewart on 9 Dec 2016.
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