Comparative study of static vs. Dynamic controls of double-skin systems

This study compares three different control strategies for double-skin systems. The analyzed control strategies are (1) rule-based approach, (2) exhaustive search and (3) gradient-based search. The fundamental principle of the rule-based approach is “if this, do that” under certain circumstances, and the rules are generally based on expert knowledge. The disadvantage of this approach is that it does not reflect the transient behaviour of the system. The exhaustive search, so called “brute force” search, tests all possibilities for the solution while the gradient method uses the derivative of the cost function. This study aims to investigate differences in the performance of the systems operating under various control strategies. The control simulation uses the lumped calibrated model developed in (Park et al., 2003a) and optimization algorithms to solve for optimal control variables to minimize a cost function over the time horizon. It was found that the rule-based approach works significantly worse in terms of decreasing the cost function in both heating and cooling modes. There is a negligible difference between the gradient and exhaustive methods. Considering significant computation time required for the exhaustive method, the gradient method is suited for optimal control for a double skin system.