Improving the control of shading blinds, lights, natural ventilation, and HVAC systems while satisfying human comfort requirements can result in significant energy cost savings with time-of-day electricity pricing. Traditionally, the above-mentioned devices are controlled separately. In this paper, a novel formulation for the integrated control and the corresponding solution methodology are presented. The problem is to minimize daily energy costs of lights and HVAC systems while satisfying equipment capacities, system dynamics, and human comfort. The problem is complicated since 1) individual rooms are coupled as they compete for the HVAC with limited capacity and nonlinear characteristics, and 2) the problem is believed to be NP-hard in view that decision variables are all discrete. A solution methodology that combines Lagrangian relaxation and stochastic dynamic programming is developed within the surrogate optimization framework to obtain near-optimal strategies. These strategies are further refined to become novel control rules for easy practical implementation. Numerical simulation results show that both of the above strategies can effectively reduce the total energy cost, and that the integrated control works better than selected traditional control strategies.
[1]
Kamel Ghali,et al.
Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm
,
2009
.
[2]
B. Ripley,et al.
Pattern Recognition
,
1968,
Nature.
[3]
Peter B. Luh,et al.
An Optimization-Based Approach for Facility Energy Management with Uncertainties
,
2005
.
[4]
X. Zhao,et al.
Surrogate Gradient Algorithm for Lagrangian Relaxation
,
1999
.
[5]
Jianing Zhao,et al.
Estimating natural-ventilation potential considering both thermal comfort and IAQ issues
,
2007
.
[6]
Geoffrey Van Moeseke,et al.
Impact of control rules on the efficiency of shading devices and free cooling for office buildings
,
2007
.