  Energy hubs convert and distribute energy resources by combining different
energy inputs through multiple conversion and storage components. The optimal
operation of the energy hub exploits its flexibility to increase the energy
efficiency and reduce the operational costs. However, uncertainties in the
demand present challenges to energy hub optimization. In this paper, we propose
a stochastic MPC controller to minimize energy costs using chance constraints
for the uncertain electricity and thermal demands. Historical data is used to
build a demand prediction model based on Gaussian processes to generate a
forecast of the future electricity and heat demands. The stochastic
optimization problem is solved via the Scenario Approach by sampling multi-step
demand trajectories from the derived prediction model. The performance of the
proposed predictor and of the stochastic controller is verified on a simulated
energy hub model and demand data from a real building.
