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SUMMARY:Electricity demand forecasting and bidding via data-driven inverse
  optimization - Juan Miguel Morales González (Universidad de Málaga)
DTSTART:20190108T133000Z
DTEND:20190108T143000Z
UID:TALK116761@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:A method to predict the aggregate demand of a cluster of price
 -responsive consumers of electricity is discussed in this presentation. Th
 e price-response of the aggregation is modelled by an optimization problem
  whose defining parameters represent a series of marginal utility curves\,
  and minimum and maximum consumption limits. These parameters are\, in tur
 n\, estimated from observational data using an approach inspired from dual
 ity theory. The resulting estimation problem is nonconvex\, which makes it
  very hard to solve. In order to obtain good parameter estimates in a reas
 onable amount of time\, we divide the estimation problem into a feasibilit
 y problem and an optimality problem. Furthermore\, the feasibility problem
  includes a penalty term that is statistically adjusted by cross validatio
 n. The proposed methodology is data-driven and leverages information from 
 regressors\, such as time and weather variables\, to account for changes i
 n the parameter estimates. The estimated price-response model is used to f
 orecast the power load of a group of heating\, ventilation and air conditi
 oning systems\, with positive results. We also show how this method can be
  easily extended to be used for demand-side bidding in electricity markets
 .
LOCATION:Seminar Room 1\, Newton Institute
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