An enhanced bat algorithm approach for reducing electrical power consumption of air conditioning systems based on differential operator

• Minimizing power consumption of multi-chiller systems is an important issue.
• Differential bat algorithm is proposed for solving optimal loading problem.
• DBA can be efficiently used to save more energy in multi-chiller systems.
• DBA finds more accurate results than the other studied methods.

Energy saving plays a vital role in the decision-making process surrounding building design. Most often, the power consumption of chillers has a significant proportion of the total power consumption of the heating, ventilating and air conditioning (HVAC) systems. The problem of efficiently managing multiple chiller systems (MCSs) in HVAC is complex in many respects. In particular, the electrical energy consumption markedly increases if the machines are not properly managed. In this paper, an extended version of optimal chiller loading (OCL), namely, daily optimal chiller loading (DOCL) is introduced where a 24-h cooling load profile should be satisfied by a number of chillers so that the total power consumption of the chillers during 24-h is minimized. Then, an efficient optimization method is proposed for solving the DOCL by means of a new enhanced differential bat algorithm (DBA) which is a swarm intelligence paradigm. The simulation results represent that DBA produces promising results in comparison with other optimization metaheuristics, such as the original BA, firefly algorithm (FA), harmony search (HS), chicken swarm optimization (CSO), differential evolution (DE) and exponential natural evolution strategy (xNES).