Three ACID Challenge Problems
Rahul Mangharam

Citation
Rahul Mangharam. "Three ACID Challenge Problems". Talk or presentation, 29, March, 2017; Keynote presentation at Comcast NBCUniversal's ACID Symposium on Algorithms, Cloud services, IoT and Big Data, Comcast Center, Philadelphia.

Abstract
This is a two-part talk which describes opportunities at the intersection of machine learning, data-driven control and the use of cloud services for a new generation of Intelligent Physical Systems.

1. Energy Systems: Data Predictive Control - Bridging Machine Learning and Control Systems In December 2014, the average price of wholesale electricity in the PJM market surged from $31/MWh to $2680/MWh - an 86X increase in 5mins. Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR approaches are predominantly manual and rule-based or involve deriving first principles based models which are extremely cost and time prohibitive to build. We have developed DR-Advisor, a Demand Response Advisor and recommendation system for energy flexibility in large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions during price spikes. Built upon DR-Advisor is IAX, an Interactive Energy Analytics engine - think of it as a Siri for querying buildings' energy use. We are developing IAX to procedurally generate energy dashboards for open-ended questions. More info at: http://mlab.seas.upenn.edu/projectsites/dr-advisor/

2. Autonomous Systems: A Driver's License Test for Driverless Vehicles Autonomous vehicles (AVs) have driven millions of miles on public roads, but even the simplest scenarios, such as a lane change maneuver, have not been certified for safety. As there is no systematic method to bound and minimize the risk of decisions made by the vehicle's decision controller, the insurance liability of autonomous vehicles currently is entirely on the manufacturer. I will describe APEX, a tool for autonomous vehicle plan verification and execution across a variety of driving scenarios. We will see the use of synthetic environments such as computer gaming to train and evaluate machine learning and decision control algorithms in future AVs.

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Citation formats

• HTML

  Rahul Mangharam. <a
  href="http://www.terraswarm.org/pubs/959.html"
  ><i>Three ACID Challenge
  Problems</i></a>, Talk or presentation,  29,
  March, 2017; Keynote presentation at Comcast NBCUniversal's
  ACID Symposium on Algorithms, Cloud services, IoT and Big
  Data, Comcast Center, Philadelphia.

• Plain text

  Rahul Mangharam. "Three ACID Challenge Problems".
  Talk or presentation,  29, March, 2017; Keynote presentation
  at Comcast NBCUniversal's ACID Symposium on Algorithms,
  Cloud services, IoT and Big Data, Comcast Center,
  Philadelphia.

• BibTeX

  @presentation{Mangharam17_ThreeACIDChallengeProblems,
      author = {Rahul Mangharam},
      title = {Three ACID Challenge Problems},
      day = {29},
      month = {March},
      year = {2017},
      note = {Keynote presentation at Comcast NBCUniversal's
                ACID Symposium on Algorithms, Cloud services, IoT
                and Big Data, Comcast Center, Philadelphia},
      abstract = {This is a two-part talk which describes
                opportunities at the intersection of machine
                learning, data-driven control and the use of cloud
                services for a new generation of Intelligent
                Physical Systems. <ul> 1. Energy Systems: Data
                Predictive Control - Bridging Machine Learning and
                Control Systems In December 2014, the average
                price of wholesale electricity in the PJM market
                surged from $31/MWh to $2680/MWh - an 86X increase
                in 5mins. Demand response (DR) is becoming
                increasingly important as the volatility on the
                grid continues to increase. Current DR approaches
                are predominantly manual and rule-based or involve
                deriving first principles based models which are
                extremely cost and time prohibitive to build. We
                have developed DR-Advisor, a Demand Response
                Advisor and recommendation system for energy
                flexibility in large buildings which involves
                predicting the demand response baseline,
                evaluating fixed rule based DR strategies and
                synthesizing DR control actions during price
                spikes. Built upon DR-Advisor is IAX, an
                Interactive Energy Analytics engine - think of it
                as a Siri for querying buildings' energy use. We
                are developing IAX to procedurally generate energy
                dashboards for open-ended questions. More info at:
                http://mlab.seas.upenn.edu/projectsites/dr-advisor/
                <p> 2. Autonomous Systems: A Driver's License Test
                for Driverless Vehicles Autonomous vehicles (AVs)
                have driven millions of miles on public roads, but
                even the simplest scenarios, such as a lane change
                maneuver, have not been certified for safety. As
                there is no systematic method to bound and
                minimize the risk of decisions made by the
                vehicle's decision controller, the insurance
                liability of autonomous vehicles currently is
                entirely on the manufacturer. I will describe
                APEX, a tool for autonomous vehicle plan
                verification and execution across a variety of
                driving scenarios. We will see the use of
                synthetic environments such as computer gaming to
                train and evaluate machine learning and decision
                control algorithms in future AVs. </ul> },
      URL = {http://terraswarm.org/pubs/959.html}
  }
  

Posted by Mary Stewart on 25 May 2017.
Groups: services