02170nas a2200217   4500008004100000245007200041210006900113260001200182300001200194490000700206520147200213653002801685653002901713653003001742653002001772653002001792653001701812100002301829700001801852856008201870       2001                        eng d00aEfficient Solution Strategies for Building Energy System Simulation0 aEfficient Solution Strategies for Building Energy System Simulat  c04/2001  a309-3170 v333 a<p>The efficiencies of methods employed in solution of building simulation models are considered and compared by means of benchmark testing. Direct comparisons between the Simulation Problem Analysis and Research Kernel (SPARK) and the HVACSIM+ programs are presented, as are results for SPARK versus conventional and sparse matrix methods. An indirect comparison between SPARK and the IDA program is carried out by solving one of the benchmark test suite problems using the sparse methods employed in that program. The test suite consisted of two problems chosen to span the range of expected performance advantage. SPARK execution times versus problem size are compared to those obtained with conventional and sparse matrix implementations of these problems. Then, to see if the results of these limiting cases extend to actual problems in building simulation, a detailed control system for a heating, ventilating and air conditioning (HVAC) system is simulated with and without the use of SPARK cut set reduction. Execution times for the reduced and non-reduced SPARK models are compared with those for an HVACSIM+ model of the same system. Results show that the graph-theoretic techniques employed in SPARK offer significant speed advantages over the other methods for significantly reducible problems and that by using sparse methods in combination with graph-theoretic methods even problem portions with little reduction potential can be solved efficiently.</p>10abuilding energy systems10acomputational efficiency10agraph theory applications10ahvac simulation10ahvacsim+ models10aspark models1 aSowell, Edward, F.1 aHaves, Philip  uhttps://simulationresearch.lbl.gov/publications/efficient-solution-strategies