• Energy Research

Abstract The goal of optimal temperature control in buildings is usually to ensure thermal comfort with minimal energy consumption. In intermittently occupied buildings, this presumes the ability of the controller to recover in due time the building from setback. Model Predictive Control (MPC) is considered among the best candidates for this task due to its ability to use occupancy schedule and weather forecasts for optimal temperature control. However, the use of the classical cost function within MPC does not allow to achieve the objectives of minimal energy consumption and optimal restart of the heating system. Therefore, a new cost function is introduced, which minimizes the energy consumption while maintaining the thermal comfort in the building. The obtained linear optimization problem is formulated to fit into the canonical form of Linear Programming method.

International audience; Ventilated slabs are Thermally Activated Building Systems (TABS) that use the supply air as the heating/cooling medium for taking advantage of the thermal inertia of a slab. The objective for the ventilated slab is to maintain the indoor temperature within the desired range for a minimal energy consumption, at least equal or lower than with a regular (non-ventilated) slab. To achieve such objectives, an efficient control is required but the latter is challenging for TABS because of the delay brought by their thermal inertia. While advanced control techniques are available and suitable with TABS, it is not always clear if the final performances are driven by the control technique itself or the inherent thermal potential of the ventilated slab. The present paper numerically analyses the behaviour of the ventilated slabs controlled with a Model Predictive Controller, and provides a comparison with a non-ventilated slab combined with the same controller. This comparison was extended to four other airflow rates, since it significantly changes the thermal dynamic of a ventilated slab. The indoor temperature requirements were satisfied most of the time and energy demand differences observed between the two systems were minor. However, a lower airflow rate is preferable as it decreases the consumption of the fans, which was significant for this test case. The most important improvement brought by the ventilated slab was the smoothed temperature variation

Abstract A building is permanently in thermodynamic non-equilibrium due to changing weather, free gains and indoor temperature set-point. Load calculation in dynamic conditions is an essential goal of building energy simulation. This paper demonstrates that the load calculation is a control problem. Supposing that the thermal model of the building is linear and that the model of the building, the weather conditions and occupational program are known in the design stage, the paper proposes an unconstrained optimal control algorithm which uses feed-forward to compensate the weather conditions and model predictive programming (MPP) for set-point tracking. MPP is obtained by modifying the dynamic matrix control (DMC), a variant of model predictive control (MPC). The peak load depends on the set-back time of the indoor temperature: smaller the set-back time, larger the peak load, but smaller energy consumption. Then, the choice of the weighting coefficients in the model predictive programming may be done on economical considerations.