Designing HVAC systems using particle swarm optimization

Abstract

Many design and operating goals in HVAC systems are optimization problems. For example, building operators want to determine the combination of equipment and set-points that result in the most energy-efficient operating conditions. Similarly, HVAC system designers want to determine the best combination of equipment, components, and operating conditions that provide minimum lifecycle cost. In both problems, the goal is to find an optimal set of system design variables. The purpose of this work is to apply an optimization technique known as particle swarm optimization to the design and operation of HVAC systems. This algorithm is based on the idea of swarms of animals, such as flying birds or insects, searching for food. In these swarms, each member gains knowledge from the whole and in turn contributes its individual knowledge back to the swarm. The result is a very efficient way to find the best available food source. The particle swarm optimization algorithm is well suited to computers with multiple processors and to HVAC problems, because it does not require continuous or well-behaved mathematical functions. Another advantage of the method is that it is remarkably simple to implement. This article illustrates the particle swarm optimization algorithm by applying it to the design of an HVAC piping system.