E3S Web of Conferences (Jan 2019)

Energy demand prediction for the implementation of an energy tariff emulator to trigger demand response in buildings

  • Noyé Sarah,
  • Saralegui Unai,
  • Rey Raphael,
  • Anton Miguel Angel,
  • Romero Ander

DOI
https://doi.org/10.1051/e3sconf/201911105025
Journal volume & issue
Vol. 111
p. 05025

Abstract

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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.