Building energy reduction based on economic model predictive control

A team of chemical engineering researchers worked together to design a new software that would demonstrate the effectiveness of an economic energy model prior to its use. To do this they designed a model predictive control (MPC) simulator; such simulator would be used to predict the performance outcome, optimize the energy use, and reduce the cost of the building heating, ventilating, and conditioning (HVAC) systems.

The Model Predictive Control works as follow: A simulated multi-zone commercial building equipped with of variable air volume (VAV) cooling system is built in Energyplus. Building Controls Virtual Test Bed (BCVTB) is the middleware needed to produce the real-time data exchange between Energyplus and Matlab. The controller is then obtained from sending and receiving sockets. To adjust the MPC framework, zone temperature and power models are introduced through a performed System identification.

The economic objective function in Model Predictive Control accounts for the daily electricity costs, which include time-of-use (TOU) energy charge and demand charge. In each time step, a min–max optimization is formulated and converted into a linear programming problem and solved. In a weekly simulation, a pre-cooling effect during off-peak period and a cooling discharge from the building thermal mass during on-peak period can be observed.

Cost savings by MPC are estimated by comparing with the baseline and other open-loop control strategies. The effect of several experimental factors in the MPC configuration is investigated and the best scenario is selected for future practical tests.

If this new Model Predictive Control system’s efficacy was proven, this would become ground breaking software for the building energy reduction industry, allowing for the prediction of energy efficiency with a minimal error yield.


Jingran Ma, Joe Qin, Timothy Salsbury, Peng Xu, Demand reduction in building energy systems based on economic model predictive control, Chemical Engineering Science, Volume 67, Issue 1, 1 January 2012, Pages 92-100;


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