01574nas a2200217 4500008004100000245012300041210006900164260001200233300001200245490000700257520083600264653002201100653001801122653002201140653001901162653001701181653003201198100002001230700002501250856008101275 2004 eng d00aA comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization
0 acomparison of deterministic and probabilistic optimization algor c08/2004 a989-9990 v393 a

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.

10acoordinate search10adirect search10agenetic algorithm10ahooke–jeeves10aoptimization10aparticle swarm optimization1 aWetter, Michael1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/comparison-deterministic-and02172nas a2200169 4500008004100000245009100041210006900132260002700201300001400228490000800242520160400250100002001854700002501874700002401899700001601923856006301939 2003 eng d00aComparison of a generalized pattern search and a genetic algorithm optimization method0 aComparison of a generalized pattern search and a genetic algorit aEindhoven, Netherlands a1401-14080 vIII3 a

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.

1 aWetter, Michael1 aWright, Jonathan, A.1 aAugenbroe, Godfried1 aHensen, Jan uhttp://www.ibpsa.org/proceedings/BS2003/BS03_1401_1408.pdf00468nas a2200121 4500008004100000245007400041210006900115490000800184100001800192700002200210700002500232856008900257 1996 eng d00aCondition Monitoring in HVAC Subsystems using First Principles Models0 aCondition Monitoring in HVAC Subsystems using First Principles M0 v1021 aHaves, Philip1 aSalsbury, Tim, I.1 aWright, Jonathan, A. uhttps://simulationresearch.lbl.gov/publications/condition-monitoring-hvac-subsystems