Judges’ Queries and Presenter’s Replies

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Presentation Discussion

  • Icon for: Vega Shah

    Vega Shah

    Trainee
    May 21, 2012 | 03:40 p.m.

    Great video!

  • Icon for: Akshay Sriprasad

    Akshay Sriprasad

    Presenter
    May 23, 2012 | 05:32 p.m.

    Thanks!

  • Icon for: Erin Baker

    Erin Baker

    Faculty
    May 22, 2012 | 09:03 a.m.

    Nice Video — sets out the issues clearly.

  • Icon for: Akshay Sriprasad

    Akshay Sriprasad

    Presenter
    May 23, 2012 | 05:32 p.m.

    Thank you!

  • Icon for: Rishee Jain

    Rishee Jain

    Trainee
    May 22, 2012 | 10:39 a.m.

    Really cool work Akshay! I am curious to know what the end goal of the ANN simulation is. In the end, are you trying to build a more accurate prediction model of energy consumption then models that already exists and what are the applications of this new model?

  • Icon for: Akshay Sriprasad

    Akshay Sriprasad

    Presenter
    May 23, 2012 | 05:25 p.m.

    Rishee,
    Thanks for the question! Our aims are two-fold regarding the ANN model. Firstly, we do aim to develop a better and more granular model of residential demand. We are able to do this due to the robust data set provided by collaboration with Pecan Street Inc., as well as manipulation of ANN structure and input variables. Secondly, we aim to deploy these forecast models in actual demand response applications, such as curtailment incentive programs, etc.

    Furthermore, the onset of such technologies such as energy storage and electric vehicles provide rich dynamics to the modeling problem, and we are very excited to handle these modeling challenges!

  • Icon for: Cory Budischak

    Cory Budischak

    Trainee
    May 22, 2012 | 04:08 p.m.

    Akshay,

    You mention that ambient temperature, relative humidity, and hour of the day were highly correlated to residential energy consumption. Did you study any other parameters to determine their correlation?
  • Icon for: Akshay Sriprasad

    Akshay Sriprasad

    Presenter
    May 23, 2012 | 05:31 p.m.

    Cory,
    Thanks for the interest! We did investigate other parameters, such as day of the week, which provided a poor fit, and we hope to include month of the year in the future. With only a year of data so far, we were unable to include the seasonal effects that a month input would have,

    When we incorporate solar generation into the model, we’ll include phenomena such as solar irradiation in as well.

  • Further posting is closed as the competition has ended.

Icon for: Akshay Sriprasad

AKSHAY SRIPRASAD

University of Texas at Austin
Years in Grad School: 2

Demand Response & Forecasting: Keys to a Smarter Grid

The case for the smart grid is becoming exceedingly clear. Firstly, grid stability issues plague the national grid, as witnessed in critical events such as the Northeast blackout of 2003. Secondly, peak generation issues plague utilities and subsequently increase energy costs to all parties involved. Finally, outdated grid infrastructure limits the utility of clean and renewable generation sources, such as solar and wind, due to intermittency issues. The smart grid aims to solve these issues through comprehensive next-generation computerization of grid operations. A key aspect of the smart grid is demand response, referring to strategic curtailment of electricity load during periods of peak demand and duress. Demand response has the potential to alleviate all of the aforementioned grid complications, yet keen insights into customer behavior and consumption patterns are required to make informed policy and business decisions. Our work investigates strategies to best utilize demand response, including customer behavioral modeling, variable pricing programs, and demand forecasting.