TY  - Journal Article
AU  - Noyé, Sarah
AU  - Saralegui, Unai
AU  - Rey, Raphael
AU  - Anton, Miguel Angel
AU  - Romero, Ander
TI  - Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings
PY  - 2019
PB  - EDP Sciences
AB  - Buildings are key actors of the electrical gird. As such they have an important role to play in grid
stabilization, especially in a context where renewable energies are mandated to become an increasingly
important part of the energy mix. Demand response provides a mechanism to reduce or displace electrical
demand to better match electrical production. Buildings can be a pool of flexibility for the grid to operate
more efficiently. One of the ways to obtain flexibility from building managers and building users is the
introduction of variable energy prices which evolve depending on the expected load and energy generation.
In the proposed scenario, the wholesale energy price of electricity, a load prediction, and the elasticity of
consumers are used by an energy tariff emulator to predict prices to trigger end user flexibility. In this paper,
a cluster analysis to classify users is performed and an aggregated energy prediction is realised using Random
Forest machine learning algorithm.
UR  - http://hdl.handle.net/11556/755
DX  - 10.1051/e3sconf/201911105025
ER  -