DR-Advisor: A data-driven demand response recommender system
Madhur Behl, Francesco Smarra, Rahul Mangharam

Citation
Madhur Behl, Francesco Smarra, Rahul Mangharam. "DR-Advisor: A data-driven demand response recommender system". Applied Energy, 170:30-46, March 2016.

Abstract
Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR approaches are predominantly completely manual and rule-based or involve deriv- ing first principles based models which are extremely cost and time prohibitive to build. We consider the problem of data-driven end-user DR for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions. The challenge is in evaluating and taking control decisions at fast time scales in order to curtail the power consumption of the building, in return for a financial reward. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of up to 380 kW and over $45,000 in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8–98.9% prediction accuracy for 8 buildings on Penn’s campus. We com- pare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy prediction.

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Citation formats

• HTML

  Madhur Behl, Francesco Smarra, Rahul Mangharam. <a
  href="http://www.terraswarm.org/pubs/756.html"
  >DR-Advisor: A data-driven demand response recommender
  system</a>, <i>Applied Energy</i>,
  170:30-46, March 2016.

• Plain text

  Madhur Behl, Francesco Smarra, Rahul Mangharam.
  "DR-Advisor: A data-driven demand response recommender
  system". <i>Applied Energy</i>, 170:30-46,
  March 2016.

• BibTeX

  @article{BehlSmarraMangharam16_DRAdvisorDatadrivenDemandResponseRecommenderSystem,
      author = {Madhur Behl and Francesco Smarra and Rahul
                Mangharam},
      title = {DR-Advisor: A data-driven demand response
                recommender system},
      journal = {Applied Energy},
      volume = {170},
      pages = {30-46},
      month = {March},
      year = {2016},
      abstract = {Demand response (DR) is becoming increasingly
                important as the volatility on the grid continues
                to increase. Current DR approaches are
                predominantly completely manual and rule-based or
                involve deriv- ing first principles based models
                which are extremely cost and time prohibitive to
                build. We consider the problem of data-driven
                end-user DR for large buildings which involves
                predicting the demand response baseline,
                evaluating fixed rule based DR strategies and
                synthesizing DR control actions. The challenge is
                in evaluating and taking control decisions at fast
                time scales in order to curtail the power
                consumption of the building, in return for a
                financial reward. We provide a model based control
                with regression trees algorithm (mbCRT), which
                allows us to perform closed-loop control for DR
                strategy synthesis for large commercial buildings.
                Our data-driven control synthesis algorithm
                outperforms rule-based DR by 17% for a large DoE
                commercial reference building and leads to a
                curtailment of up to 380 kW and over $45,000 in
                savings. Our methods have been integrated into an
                open source tool called DR-Advisor, which acts as
                a recommender system for the building’s
                facilities manager and provides suitable control
                actions to meet the desired load curtailment while
                maintaining operations and maximizing the economic
                reward. DR-Advisor achieves 92.8–98.9%
                prediction accuracy for 8 buildings on Penn’s
                campus. We com- pare DR-Advisor with other data
                driven methods and rank 2nd on ASHRAE’s
                benchmarking data-set for energy prediction.},
      URL = {http://terraswarm.org/pubs/756.html}
  }
  

Posted by Elizabeth Coyne on 11 Mar 2016.
Groups: pw