RT Conference Proceedings
T1 Predicting the electricity demand response via data-driven inverse optimization
A1 Morales-González, Juan Miguel
A1 Saez-Gallego, Javier
K1 Programación matemática
AB A method to predict the aggregate demand of a cluster of price-responsive consumers of electricity is discussed in this presentation. The price-response of the aggregation is modeled by an optimization problem whose defining parameters represent a series of marginal utility curves, and minimum and maximum consumption limits. These parameters are, in turn, estimated from observational data using an approach inspired from duality theory. The resulting estimation problem is nonconvex, which makes it very hard to solve. In order to obtain good parameter estimates in a reasonable amount of time, we divide the estimation problem into a feasibility problem and an optimality problem. Furthermore, the feasibility problem includes a penalty term that is statisticallyadjusted by cross validation. The proposed methodology is data-driven and leverages information from regressors, such as time and weather variables, to account for changes in the parameter estimates. The estimated price-response model is used to forecast the power load of a group of heating, ventilation and air conditioning systems, with positive results.
YR 2018
FD 2018-07-06
LK https://hdl.handle.net/10630/16157
UL https://hdl.handle.net/10630/16157
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. European Research Council: This research work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 755705).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026