Abstract
Reducing energy consumption is one of the most important for optimal electric-driven chiller operation. Therefore, even small reduction in power consumption will achieve significant energy savings. This paper adopts improved particle swarm optimization (IPSO), which is aiming to reduce energy consumption, and improve the performance of chillers. The method has been validated by real case study, and the results have demonstrated the effectiveness for saving energy and kept the cooling demand at satisfactory level.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lu L et al (2005) Global optimization for overall HVAC systems—Part I problem formulation and analysis. Energy Convers Manag 46(7):999–1014
Pérez-Lombard L, Ortiz J, Pout C (2008) A review on buildings energy consumption information. Energy Build 40(3):394–398
Torzhkov A et al (2010) Chiller plant optimization-an integrated optimization approach for chiller sequencing and control. In: 2010 49th IEEE conference on Decision and Control (CDC). IEEE
Ardakani AJ, Ardakani FF, Hosseinian S (2008) A novel approach for optimal chiller loading using particle swarm optimization. Energy Build 40(12):2177–2187
Lee W-S, Lin L-C (2009) Optimal chiller loading by particle swarm algorithm for reducing energy consumption. Appl Therm Eng 29(8):1730–1734
Chang Y-C et al (2006) Simulated annealing based optimal chiller loading for saving energy. Energy Convers Manag 47(15):2044–2058
Chang Y-C (2006) An innovative approach for demand side management—optimal chiller loading by simulated annealing. Energy 31(12):1883–1896
Li BF et al (2012) The application of simulated annealing in chiller energy consumption optimization. In: Advanced materials research. Trans Tech Publications
Lee W-S, Chen Y-T, Kao Y (2011) Optimal chiller loading by differential evolution algorithm for reducing energy consumption. Energy Build 43(2):599–604
Sulaiman MH et al (2014) A new swarm intelligence approach for optimal chiller loading for energy conservation. Procedia-Social Behav Sci 129:483–488
dos Santos Coelho L, Mariani VC (2013) Mariani, improved firefly algorithm approach applied to chiller loading for energy conservation. Energy Build 59:273–278
Turgut OE, Turgut MS, Çoban MT (2015) Artificial cooperative search algorithm for optimal loading of multi-chiller systems. Turkish J Eng Sci Technol 3 (2015) (2015 ed., Turkey: TUJEST. 20)
Jin X, Du Z, Xiao X (2007) Energy evaluation of optimal control strategies for central VWV chiller systems. Appl Therm Eng 27(5):934–941
Ma Z, Wang S (2009) An optimal control strategy for complex building central chilled water systems for practical and real-time applications. Build Environ 44(6):1188–1198
Beghi A et al (2012) A PSO-based algorithm for optimal multiple chiller systems operation. Appl Therm Eng 32:31–40
Wei X et al (2015) Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance. Energy 83:294–306
Hamid E et al (2015) Optimal operation of multi-chillers for energy saving using a multi-fuzzy inference system. In: Applied mechanics and materials. Trans Tech Publications
Hamid E et al (2015) A new strategy for multiple chillers plant operation using fuzzy inference system. In: Applied Mechanics and Materials. Trans Tech Publications
Chang Y-C (2006) An outstanding method for saving energy-optimal chiller operation. Energy Convers IEEE Trans 21(2):527–532
Almassalkhi M, Simon B, Gupta A (2014) A novel online energy management solution for energy plants. In: IEEE Power Systems Conference (PSC). Clemson University.
Business energy Advisor, Cent and Sc Chillers. http://www.ouc.bizenergyadvisor.com
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE International Conference on Neural Networks, 1995 Proceedings. IEEE
Chang W (2010) A novel particle swarm optimization for optimal scheduling of hydrothermal system. Energy Power Eng 2(04):223
Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, 1999. CEC 99. IEEE
Lee KY, El-Sharkawi MA (2008) Modern heuristic optimization techniques: theory and applications to power systems, vol 39. Wiley
Malik RF et al (2007) New particle swarm optimizer with sigmoid increasing inertia weight. Int J Comput Sci Security 1(2):35–44
Kannan S et al (2004) Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electr Power Syst Res 70(3):203–210
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Nallagownden, P., Hamid Abdalla, E.A., Mohd Nor, N., Romlie, M.F. (2017). Optimal Chiller Loading Using Improved Particle Swarm Optimization. In: Ibrahim, H., Iqbal, S., Teoh, S., Mustaffa, M. (eds) 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 398. Springer, Singapore. https://doi.org/10.1007/978-981-10-1721-6_12
Download citation
DOI: https://doi.org/10.1007/978-981-10-1721-6_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1719-3
Online ISBN: 978-981-10-1721-6
eBook Packages: EngineeringEngineering (R0)