  Nowadays, Energy Management Systems (EMS) are accessible for homes and
buildings to optimize energy consumption especially when solar panels and
batteries are installed. The intelligence of existing systems is often based on
environmental or exogenous information like the weather, energy prices, and
endogenous information like user consumption behavior and activity. The
solutions aim to adapt a consumption profile to the produced energy in order to
reduce costs. In the case of a perfect prediction of all variables, system
performance can be controlled. In this article, we study the impact of
generation prediction error on the daily energy cost. For this, we consider the
energy management system as a black-box and we simulate multiple scenarios with
different prediction errors using the quasi-random Monte Carlo method. We
observe the global sensitivity of the system by measuring the Sobol indices in
order to identify errors that impact more the daily energy cost. The analyses
are based on French consumption data and on irradiance data for Carpentras,
France. Results show that findings are aligned with battery charge and
discharge strategies.
