@article {75, title = {Co-simulation for performance prediction of integrated building and HVAC systems - An analysis of solution characteristics using a two-body system}, journal = {Simulation Modelling Practice and Theory}, volume = {18}, year = {2010}, month = {08/2010}, pages = {957-970}, chapter = {957}, abstract = {

Integrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC) systems can help in reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation to integrate different BPS tools. Co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration.

This article analyzes how co-simulation influences consistency, stability and accuracy of the numerical approximation to the solution. Consistency and zero-stability are studied for a general class of the problem, while a detailed consistency and absolute stability analysis is given for a simple two-body problem. Since the accuracy of the numerical approximation to the solution is reduced in co-simulation, the article concludes by discussing ways for how to improve accuracy.

}, doi = {10.1016/j.simpat.2010.02.011}, author = {Marija Trcka and Jan Hensen and Michael Wetter} } @article {68, title = {Co-simulation of innovative integrated HVAC systems in buildings}, journal = {Journal of Building Performance Simulation}, volume = {2}, year = {2009}, pages = {209-230}, chapter = {209}, abstract = {

Integrated performance simulation of buildings HVAC systems can help in reducing energy consumption and increasing occupant comfort. However, no single building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to analyze integrated building systems and to enable rapid prototyping of innovative building and system technologies. One way to alleviate this problem is to use co-simulation, as an integrated approach to simulation. This article elaborates on issues important for co-simulation realization and discusses multiple possibilities to justify the particular approach implemented in the here described co-simulation prototype. The prototype is validated with the results obtained from the traditional simulation approach. It is further used in a proof-of-concept case study to demonstrate the applicability of the method and to highlight its benefits. Stability and accuracy of different coupling strategies are analyzed to give a guideline for the required coupling time step.

}, keywords = {building performance simulation, co-simulation, hvac simulation, innovative building system modelling and simulation}, doi = {10.1080/19401490903051959}, url = {http://www.informaworld.com/smpp/section?content=a913244253\&fulltext=713240928}, author = {Marija Trcka and Jan Hensen and Michael Wetter} } @proceedings {63, title = {Comparison of co-simulation approaches for building and HVAC/R system simulation.
}, year = {2007}, month = {09/2007}, pages = {1418-1425}, address = {Beijing, China}, abstract = {Appraisal of modern performance-based energy codes, as well as heating, ventilation, air- conditioning and refrigeration (HVAC/R) system*design require use of an integrated building and system performance simulation program. However, the required scope of the modeling library of such integrated tools often goes beyond those offered in available simulation programs. One remedy for this situation would be to develop the required models in an existing simulation program. However, due to the lack of model interoperability, the model would not be available in other simulation programs. We suggest co-simulation for HVAC/R system simulation as an approach to alleviate the above issues. In co-simulation, each subsystem is modeled and simulated in the appropriate simulation program, potentially on different computers, and intermediate results are communicated over the network during execution time. We discuss different co-simulation approaches and give insights into specific prototypes. Based on the prototypes, we compare the approaches in terms of accuracy, stability and execution time, using a simple case study. We finish with results discussions and recommendations on how to perform co-simulation to maintain the required accuracy of simulation results.}, url = {http://www.ibpsa.org/proceedings/BS2007/p503_final.pdf}, author = {Marija Trcka and Michael Wetter and Jan Hensen} } @conference {3390, title = {Comparison of Co-Simulation Approaches for Building and HVAC/R System Simulation}, booktitle = {Proc. Building Simulation 2007}, year = {2007}, month = {09/2007}, address = {Beijing, China}, author = {Marija Trcka and Michael Wetter and Jan Hensen} } @proceedings {49, title = {Comparison of a generalized pattern search and a genetic algorithm optimization method}, volume = {III}, year = {2003}, pages = {1401-1408}, address = {Eindhoven, Netherlands}, abstract = {

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}, 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} }