Design of optimum reference temperature profiles for energy saving control of indoor temperature in a building

Sumera I. Chaudhry; Manohar Das; Sumera I. Chaudhry; Manohar Das

doi:10.3934/energy.2016.6.906

AIMS Energy

2016, Volume 4, Issue 6: 906-920. doi: 10.3934/energy.2016.6.906

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Research article Topical Sections

Design of optimum reference temperature profiles for energy saving control of indoor temperature in a building

Sumera I. Chaudhry ,
Manohar Das ^,

Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USA

Received: 18 August 2016 Accepted: 18 November 2016 Published: 30 November 2016

This paper presents a technique for designing optimum reference temperature profiles for energy-efficient control of indoor air temperature in buildings. Arbitrarily chosen reference temperature profiles are often fraught with undesirable consequences, such as thermal discomfort for a building’s occupants or high consumption of fuels and electricity. An optimized reference temperature profile, on the other hand, attempts to seek a desired trade-off between the level of discomfort and amount of energy consumed. Also, the use of such optimized temperature profiles for adaptive control of indoor building temperature is discussed in details and some simulation results are presented.
- Building temperature control,
- Energy saving control,
- optimum temperature profile,
- adaptive temperature control
Citation: Sumera I. Chaudhry, Manohar Das. Design of optimum reference temperature profiles for energy saving control of indoor temperature in a building[J]. AIMS Energy, 2016, 4(6): 906-920. doi: 10.3934/energy.2016.6.906

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Abstract

This paper presents a technique for designing optimum reference temperature profiles for energy-efficient control of indoor air temperature in buildings. Arbitrarily chosen reference temperature profiles are often fraught with undesirable consequences, such as thermal discomfort for a building’s occupants or high consumption of fuels and electricity. An optimized reference temperature profile, on the other hand, attempts to seek a desired trade-off between the level of discomfort and amount of energy consumed. Also, the use of such optimized temperature profiles for adaptive control of indoor building temperature is discussed in details and some simulation results are presented.

References

[1]	Paris B, Eynard J, Grieu S, et al. (2010) Heating control schemes for energy management in buildings. Energ Buildings 42: 1908-1917.
[2]	Dounis A, Caraiscos C (2009) Advanced control systems engineering for energy and comfort management in a building environment-A review. Renew Sust Energ Rev 13: 1246-1261.
[3]	Moon J, Jung S, Kim Y, et al. (2011) Comparative study of artificial intelligence based building thermal control methods-application of fuzzy, adaptive neuro-fuzzy inference system, and artificial neural network. Appl Therm Eng 31: 2422-2429. doi: 10.1016/j.applthermaleng.2011.04.006
[4]	Afram A, Sarifi F (2014) Theory and Applications of HVAC Control Systems – A Review of Model Predictive Control. Build Environ 72: 343-355. doi: 10.1016/j.buildenv.2013.11.016
[5]	Hazyuk I, Ghiaus C (2014) Model predictive Control of thermal comfort as a benchmark for controller performance. Automat Constr 43: 98-109.
[6]	Privara, S, Siroky J, Ferkl L, et al. (2011) Model Predictive Control of a building heating system: The first experience. Energ Buildings 43: 564-572. doi: 10.1016/j.enbuild.2010.10.022
[7]	Balan R, Cooper J, Chao K, et al. (2011) Parameter identification and model based predictive control of temperature inside a house. Energ Build 43: 748-758.
[8]	Ma Y, Borrelli F, Hencey B, et al. (2011) Haves P. Model predictive control for the operation of building cooling systems. IEEE Trans Contr Syst Technol 99: 1-8.
[9]	Calvino F, Gennusa M, Morale M, et al. (2010) Comparing different control strategies for Indoor thermal comfort aimed at the evaluation of the energy cost of quality of building. Appl Therm Eng 30: 2386-2395. doi: 10.1016/j.applthermaleng.2010.06.008
[10]	Orosa J (2011) A new modeling methodology to control HVAC systems. Expert Syst Appl 38: 4505-4513. doi: 10.1016/j.eswa.2010.09.124
[11]	Chaudhry S, Das M (2014) A Stable Adaptive Control Scheme for Building Heating and Cooling Systems. J Power Energy Eng 2: 14-25.
[12]	Hongli1 L, Peiyong D, Qingmei Y, et al. (2012) A Novel Adaptive Energy-efficient Controller For the HVAC Systems. Proceedings of 24^th CCDC, 1402-1406.
[13]	Perrera D, Pfeiffer C, Skeie N (2014) Control of temperature and energy consumption in buildings—A review. Int J Energy Environ 5: 471-484.
[14]	Wang S, Gao D, Sun Y, et al. (2013) An online adaptive optimal control strategy for complex building chilled water systems involving intermediate heat exchangers. Appl Therm Eng 50: 614-628. doi: 10.1016/j.applthermaleng.2012.06.010
[15]	Lu L, Cai W, Chai Y, et al. (2005) Global optimization for overall HVAC systems––Part I problem formulation and analysis. Energ Convers Manage 46: 999-1014. doi: 10.1016/j.enconman.2004.06.012
[16]	Lu L, Cai W, Chai Y, et al. (2005) Global optimization for overall HVAC systems––Part II problem solution and simulations. Energ Convers Manage 46: 1015- 1028. doi: 10.1016/j.enconman.2004.06.011
[17]	Kusiak A, Li M, Tang F (2010) Modeling and optimization of HVAC energy consumption. Appl Energy 87: 3092-3102. doi: 10.1016/j.apenergy.2010.04.008
[18]	Ghiaus C (2013) Causality issue in the heat balance method for calculating the design heating and cooling load. Energy 50: 292-301. doi: 10.1016/j.energy.2012.10.024
[19]	IBPT International Building Physics Toolbox in Simulink. Available from: http://www.ibpt.org
[20]	Fanger P (1972) Thermal Comfort: Analysis and Applications in Environmental Engineering, New York: McGraw-Hill.
[21]	Ashrae ANSI, Thermal Environmental Conditions for Human Occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Atlanta, GA, 2004
[22]	Marler R, Arora J (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip O 26: 369-395. doi: 10.1007/s00158-003-0368-6
[23]	Rao S (2009) Engineering Optimization: Theory and Practice, 4^th ed., New Jersey: John Wiley and Sons.
[24]	Gennert M, Yuille A (1988) Determining The Optimal Weights In Multiple Objective Function Optimization. Proceedings of Second International Conference on Computer Vision, 87-89.
[25]	Goodwin G, Sin K (1984) Adaptive filtering prediction and control, New Jersey: Prentice-Hall.

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