Energy Efficiency

, Volume 12, Issue 5, pp 1183–1201 | Cite as

Optimized scheduling for an air-conditioning system based on indoor thermal comfort using the multi-objective improved global particle swarm optimization

  • Mohamad Fadzli Haniff
  • Hazlina SelamatEmail author
  • Nuraqilla Khamis
  • Ahmad Jais Alimin
Original Article


In energy management system (EMS), the scheduling of air-conditioning (AC) system has been shown to reduce considerable amount of its power consumption with relatively low implementation cost. However, most scheduling methods lack a systematic approach to ensuring optimal power consumption reduction and comfort experienced by occupants. The main contribution of this paper is a new optimized AC scheduling approach that focuses on indoor thermal comfort using a new multi-objective optimization algorithm, called the improved global particle swarm optimization (IGPSO), which able to find better optimal solutions faster than its original version, the global particle swarm optimization (GPSO) algorithm. IGPSO is used to model the building characteristics and to find optimum indoor temperature values for the room/building. The proposed technique is based on predicted mean vote (PMV) comfort index that is able to reduce AC power consumption while maintaining indoor comfort throughout its operation. The schedule is set in advance by making use of weather forecast and the estimation of building characteristic parameters. This technique can be implemented on existing buildings with existing HVAC systems with minimal modifications to the HVAC infrastructure. Experimental results show that the proposed method is able to provide good PMV while consuming less power compared to the commonly used extended pre-cooling technique.


HVAC scheduling Particle swarm optimization Thermal comfort HVAC power consumption Predicted mean vote Energy management system 


a1, a2, a3

Coefficients for building characteristic for tstart

b1, b2

Coefficients for building characteristic for tstop

C1, C2, C3

Acceleration coefficients


Particle’s fitness at the ith iteration


Clothing surface area factor


Convective heat transfer coefficient


Clothing insulation




1st objective’s maximum particle fitness


2nd objective’s maximum particle fitness


Metabolic rate


Number of particles


Number of archived particles


Item in population


Water vapor partial pressure


Relative air humidity

R1, R2R3

Uniform-distributed random numbers between 0 and 1


Random integer from 1 to D


Random integer from 1 to i


Random integer from 1 to N


Air temperature


Clothing surface temperature


Final Tair


Initial Tair


Mean radiant temperature


Outdoor air temperature


Setpoint temperature


AC system full operating duration


Early-on duration


Early-off duration


Particles velocity at the ith iteration


Relative air velocity


Electrical power consumption


Effective mechanical power


Particle position at the ith iteration

\( {X}_i^A \)

Archived particle position at the ith iteration

\( {X}_i^G \)

Particle’s global best position at the ith iteration

\( {X}_i^L \)

Particle’s local best position at the ith iteration


ith optimization variable


Improvement factor

\( {\gamma}_i^n \)

Improvement factor of the nth particle at the ith iteration


Standard deviation error


Particle’s standard deviation


Archived particle’s standard deviation


Coefficient acceleration factor


Coefficient acceleration factor for the ith iteration



The authors would like to thank Universiti Teknologi Malaysia and the Ministry of Higher Education Malaysia for their supports.

Funding information

The research is funded by Research University Grant 13H78.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Mohamad Fadzli Haniff
    • 1
  • Hazlina Selamat
    • 2
    Email author
  • Nuraqilla Khamis
    • 2
  • Ahmad Jais Alimin
    • 3
  1. 1.Electrical, Electronics and Automation SectionUniversiti Kuala Lumpur Malaysian Spanish InstituteKulimMalaysia
  2. 2.Centre for Artificial Intelligence & Robotics (CAIRO)Universiti Teknologi MalaysiaKuala LumpurMalaysia
  3. 3.Department of Energy and Thermofluid Engineering, Faculty of Mechanical and Manufacturing EngineeringUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia

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