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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Citation formats

• 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.
Groups: pw