Demand Response programs have been assuming lot of importance in the simulations of electric users’ loads’ profiles. The evolution of these simulations helps defining new models able to predict power consumption trends for different user types. In order to better match consumption and production energy curves, highly precise forecasts of loads’ profiles are needed. This goal can be achieved also thanks to the study of the elasticity factor, that identifies the will of a user to have his consumptions reduced after a remuneration. In this paper, a way to obtain it has been presented, together with an interpolation able to predict it. Its definition is also supposed to help building scenarios that consider the impact of the long-term use of RTP remuneration (Real Time Price). Importance of having a real-time elasticity value able to adapt to specific situations is discussed, as for example user’s habits during the weekends or weekdays and weather forecasts.
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