Building and HVAC system design can significantly improve if numerical optimization is used. However, if a cost function that is smooth in the design parameter is evaluated by a building energy simulation program, it usually becomes replaced with a numerical approximation that is discontinuous in the design parameter. Moreover, many building simulation programs do not allow obtaining an error bound for the numerical approximations to the cost function. Thus, if a cost function is evaluated by such a program, optimization algorithms that depend on smoothness of the cost function can fail far from a minimum.

For such problems it is unclear how the Hooke-Jeeves Generalized Pattern Search optimization algorithm and the simple Genetic Algorithm perform. The Hooke-Jeeves algorithm depends on smoothness of the cost function, whereas the simple Genetic Algorithm may not even converge if the cost function is smooth. Therefore, we are interested in how these algorithms perform if used in conjunction with a cost function evaluated by a building energy simulation program.

In this paper we show what can be expected from the two algorithms and compare their performance in minimizing the annual primary energy consumption of an office building in three locations. The problem has 13 design parameters and the cost function has large discontinuities. The optimization algorithms reduce the energy consumption by 7\% to 32\%, depending on the building location. Given the short labor time to set up the optimization problems, such reductions can yield considerable economic gains.

}, url = {http://www.ibpsa.org/proceedings/BS2003/BS03_1401_1408.pdf}, author = {Michael Wetter and Jonathan A. Wright}, editor = {Godfried Augenbroe and Jan Hensen} } @proceedings {48, title = {A convergent optimization method using pattern search algorithms with adaptive precision simulation}, journal = {Proceedings of the 8th IBPSA Conference}, volume = {III}, year = {2003}, pages = {1393-1400}, address = {Eindhoven, Netherlands}, abstract = {In solving optimization problems for building design and control, the cost function is often evaluated using a detailed building simulation program. These programs contain code features that cause the cost function to be discontinuous. Optimization algorithms that require smoothness can fail on such problems. Evaluating the cost function is often so time-consuming that stochastic optimization algorithms are run using only a few simulations, which decreases the probability of getting close to a minimum. To show how applicable direct search, stochastic, and gradient-based optimization algorithms are for solving such optimization problems, we compare the performance of these algorithms in minimizing cost functions with different smoothness. We also explain what causes the large discontinuities in the cost functions.

}, keywords = {coordinate search, direct search, genetic algorithm, hooke{\textendash}jeeves, optimization, particle swarm optimization}, author = {Michael Wetter and Elijah Polak}, editor = {Godfried Augenbroe and Jan Hensen} }