Model predictive control strategy applied to different types of building for space heating

Abstract

In recent years, the concept of energy-efficient buildings has attracted widespread attention due to growing energy consumption in different types of buildings. The application of thermal energy storage (TES) systems, especially latent heat energy storage (LHES), has become a promising approach to improve thermal efficiency of buildings and hence reduces CO 2 emissions. One way to achieve this could be by implementing a model predictive control (MPC) strategy, using weather and electricity cost predictions. To this end, a heat exchanger unit containing a phase change material (PCM) as a LHES medium, thermally charged by 2 solar energy was incorporated into three versions of a standard building. This paper reports on the use of EnergyPlus software to simulate the heating demand profile of these buildings, with Solving Constraint Integer Programs (SCIP) as the optimization tool. After applying the MPC strategy, the energy costs of different building types were evaluated. Furthermore, the effect of the prediction horizon and decision time step of the MPC strategy, and PCM mass capacity on the performance of the MPC were all investigated in 1 and 7-day simulations. The results showed that by increasing the prediction horizon and PCM mass, more cost saving could be obtained. However, in terms of decision time step, although the study revealed that increasing it led to a higher energy saving, it made the system more sensitive to sharp changes as it failed to provide an accurate reading of the parameters and variables.

Deep Reinforcement Learning for Autonomous Water Heater Control

“…For example, Tarragona [18] proposed an MPC strategy to improve the operation of a space-heating system coupled with renewable resources. Gholamibozanjani et al [19] applied an MPC strategy for controlling a solar-assisted active HVAC system to minimize heating costs while providing the required comfortable temperature. Starke et al [20] proposed an MPC-based control approach for heat pump water heaters to reduce electricity cost while maintaining the comfort and reducing the cycling of the water heater.…”

Deep Learning Optimal Control for a Complex Hybrid Energy Storage System

“…Even though MPC is able to reach optimal or quasi-optimal solutions, its implementation for complex systems is challenging. Aside from its computational requirements that can difficult a real-time control, MPC optimization problems usually require to be formulated as mixed integer non-linear programming (MINLP) problems [5,6], requiring specialized solvers to find optimal solutions as SCIP [7,8]. Current state-of-the-art solvers only deal with certain type of non-linearities, making it sometimes hard or impossible to express a complex system as a quasi-linear system.…”

Issues of heat supply quality improvement based on ambient air temperature forecasts and account of the heat supply system particularity

“…In the paper [6], Nora Cadau et al call attention to the fact that the use of intelligent control methods for solving local control problems is not effective, because it deprives the system of flexibility due to the fact that it leads to the use of information about the past behavior of the system. Using the same predictive control, based on forecasting the future development of the control system, and constant additional training of the model on the incoming data allows you to achieve the necessary flexibility, as well as, as shown in the paper of Gohar Gholamibozanjani et al [7], to obtain more efficient and economical use of thermal energy. The use of predictive models, as shown by Sven Fielsch et al in the paper [8] by the example of several premises heating, also allows you to take into account specific features of heated premises.…”