Review on HVAC System Optimization Towards Energy Saving Building Operation

Hazlina Selamat, Mohamad Fadzli Haniff, Zainon Mat Sharif, Seyed Mohammad Attaran, Fadhilah Mohd Sakri, Muhammad Al’Hapis Abdul Razak

Abstract


Works on the optimization of heating, ventilation and air-conditioning (HVAC) systems have been done extensively because of its high amount of building electrical energy usage. This paper provides a review on the optimizations works of HVAC systems based on three main approaches – HVAC operational parameters optimization, HVAC controller parameters optimization and building design parameters optimization. For the system’s operational parameters, the optimization is based on the HVAC’s conventional and predictive energy consumption models which is clear the predictive HVAC system models can get better response to reduce energy consumption compare to conventional energy consumption model. In most works, the thermal comfort model, either indicated by the indoor air quality (IAQ) or the predicted mean vote (PMV) was included. It is be noticed that between IAQ comfort index and PMV comfort index the PMV had a better response that can get 46% reduce the energy consumption. In addition, in the HVAC’s controller optimization approach, its objective is to improve the output response of the HVAC system in order to avoid unnecessary energy usage by optimizing the controller parameters that employ controllers such as Fuzzy Logic, Neural Network and Proportional-Integral-Derivative (PID) controllers. It is clear that among the different controller optimizations mentioned above the fuzzy logic tuning optimization has a better response to reduction of energy consumption rather than other controller optimization approach. Meanwhile, the optimization of building design parameters approach is done before the construction of the buildings so as to reduce the energy consumption, where factors such as HVAC system type, construction material type and window dimensions are determined through the optimization process. This paper reviews the works based on the three approaches of HVAC system optimizations with the objective of reducing energy usage without sacrificing the comfort of occupants inside the building that is recommended to use predictive HVAC system approaches with fuzzy logic controller. Moreover, comparing different tools for building parameter and design optimization including SEDICAE, EXRETopt and EneryPlus, the EXRETopt by using PMV comfort index makes to 62% reduction of energy consumption.

Keywords


energy consumption; HVAC optimization; thermal comfort

Full Text:

PDF

References


Haniff M.F. and H. Selamat. 2013. Review of HVAC scheduling techniques for buildings towards energy-efficient and cost-effective operations. Renewable and Sustainable Energy Reviews 27(1): 94–103.

Rahman M.M. and M.G. Rasul. 2010. Energy conservation measures in an institutional building in sub-tropical climate in Australia. Applied Energy 87(1): 2994–3004.

Andrew K., Guanglin X., and Zijun Z., 2014. Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method. Energy Conversion and Management 85(1): 146–153.

Joseph C.L., Kevin K.W.W., and Liu Y., 2008. Sensitivity analysis and energy conservation measures implications. Energy Conversion and Management 49(11): 3170–3177.

Zapater M. and P.Arroba. 2015. Energy Aware Policies in Ubiquitous Computing Facilities. In: Terzo, O. and L. Mossucca Cloud Computing With E-Science Applications. Boca Raton, Florida, United States: CRC Press, pages 267-286.

Alves O., Monteiro E., Brito P., and Romano P., 2016. Measurement and classification of energy efficiency in HVAC systems. Energy and Buildings 130: 408–419

Alibabaei N., Fung A., Raahemifar K., and Moghimi A., 2017. Effects of intelligent strategy planning models on residential HVAC system energy demand and cost during the heating and cooling seasons. Applied Energy 185: 29–43.

Oropeza-Perez I., 2016 Comparative economic assessment of the energy performance of air-conditioning within the Mexican residential sector. Energy Reports 2(1): 147–154.

Fanger P.O., 2017. Thermal Comfort: Analysis and Applications in Environmental Engineering. University of Michigan: Danish Technical Press.

Bradshaw V., 2006. The Building Environment: Active and Passive Control Systems. 3rd Edition. Singapore: John Wiley and Sons.

Ismail A.S. and R. Zulkifli. 2012. A review on thermal comfort assessment in Malaysian industries. Jurnal Teknologi (Sciences & Engineering) 59(2): 7–11.

Kamaruzzaman, K. and Samsul M., 2013. Thermal comfort assessment of a classroom in tropical climate conditions. Recent Advances in Energy, Environment and Development 88–91.

Azizpour F. and S. Moghimi. 2011. Objective and subjective assessments of thermal comfort in hot-humid region. In Proceedings of 5th WSEAS international conferences on Recent Researches in Chemistry, Biology, Environment and Culture, Montreux, Switzerland, pp. 207–210.

American Society of Heating, Refrigerating, and Air- Conditioning Engineers, Inc., 2009. Indoor Air Quality Guide.

American Society of Heating, Refrigerating, and Air- Conditioning Engineers, Inc., 2009. Ventilation for Acceptable Indoor Air Quality. Atlanta, GA.

Lu L., Cai W., Xie L., Li S., and Soh Y.C., 2005. HVAC system optimization—in-building section. Energy and Buildings 37(1): 11–22.

Kusiak A., Li M., and Tang F., 2010. Modeling and optimization of HVAC energy consumption. Applied Energy 87(10): 3092–3102.

Wang J., Huang G., Sun Y., and Liu X., 2016. Event-driven optimization of complex HVAC systems. Energy and Buildings 133(1): 79–87.

Zheng G.R. and M. Zaheer-Uddin. 1996. Optimization of thermal processes in a variable air volume HVAC system. Energy 21(5): 407–420.

Du Z., Jin X., and Fan B., 2015. Evaluation of operation and control in HVAC (heating, ventilation and air conditioning) system using exergy analysis method. Energy 89: 372–381.

Du Z., Jin X., Fang X., and Fan B., 2016. A dual-benchmark based energy analysis method to evaluate control strategies for building HVAC systems. Applied Energy 183: 700–714.

He X., Zhang Z., and Kusiak A., 2014. Performance optimization of HVAC systems with computational intelligence algorithms. Energy and Buildings 81: 371–380.

Wei X., Kusiak A., Li M., Tang F., and Zeng Y., 2015. Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance. Energy 83: 294–306.

Fiorentini M., Wall J., Ma Z., Braslavsky J., and Cooper P., 2017. Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage, Applied Energy 187: 465–479.

Ruano A., Pesteh S., Silva S., Duarte H., Mestre G., Ferreira P.M., Khosravani H., and Horta R., PVM-based intelligent predictive control of HVAC systems. IFAC-Papers On Line 49(5): 371–376.

Dobbs J. and B. Hencey. 2014. Model predictive HVAC control with online occupancy model. Energy and Buildings 82: 675–684.

Risbeck M., Maravelias C., and Rawlings J., Real-time mixed-integer optimization for improved economic performance in HVAC systems. Computer Aided Chemical Engineering 44: 33–42.

He X., Zhang Z., and Kusiak A., 2014. Performance optimization of HVAC systems with computational intelligence algorithms. Energy and Buildings 81: 371–380.

Zeng Y., Zhang Z., and Kusiak A., 2015. Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms. Energy 86: 393–402.

Kusiak A., Tang F., and Xu G., 2011. Multi-objective optimization of HVAC system with an evolutionary computation algorithm. Energy 36(5): 2440–2449.

Kusiak A., Xu G., and Tang F., 2011. Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm. Energy 36(10): 5935–5943.

Kusiak A. and G. Xu. 2012. Modeling and optimization of HVAC systems using a dynamic neural network. Energy 42(1): 241–250.

Cigler J., Prívara S., Váňa Z., Žáčeková E. and Ferkl L., 2012. On predicted mean vote optimization in building climate control. In 2012 20th Mediterranean Conference on Control & Automation (MED), Barcelona, Spain. pp. 1518–1523.

Cigler J., Prívara S., Váňa Z., Komárková D. and Šebek M., 2012. Optimization of predicted mean vote thermal comfort index within model predictive control framework. In 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), Maui, HI. pp. 3056–3061.

Golshan M., Thoen H., and Zeiler W., 2018. Dutch sustainable schools towards energy positive. Journal of Building Engineering 19: 161–171.

Haniff M.F., Selamat H., Khamis H., and Alimin, A.J., 2018. Optimized scheduling for an air-conditioning system based on indoor thermal comfort using the multi-objective improved global particle swarm optimization. Energy Efficiency: 1–19.

Mei J., Xia X., and Song M., 2018. An autonomous hierarchical control for improving indoor comfort and energy efficiency of a direct expansion air conditioning system. Applied Energy 221: 450–463.

Lee J.N., Lin T.M., and Chen C.C., 2014. Modeling validation and control analysis for controlled temperature and humidity of air conditioning system. The Scientific World Journal.

Tashtoush B., Molhim M., and Al-Rousan M, 2005. Dynamic model of an HVAC system for control analysis. Energy 30(10): 1729–1745.

NSW Government. Office of Environment and Heritage, 2015. I Am Your Optimisation Guide: Heating, Ventilation and Air-Conditioning System. Sydney, Australia.

Aswani A, Master N., Taneja J., Culler D. and Tomlin C., 2012. Reducing transient and steady state electricity consumption in HVAC using learning-based model-predictive control. In Proceedings of the IEEE 100(1): 240–253.

Asad H.S., Yuen R.K.K., and Huang G., 2017. Multiplexed real-time optimization of HVAC systems with enhanced control stability. Applied Energy 187: 640–651.

Asad H.S., Yuen R.K.K., and Huang G., 2016. Degree of freedom based set-point reset scheme for HVAC real-time optimization. Energy and Buildings 128: 349–359.

Hussain S, Gabbar H., Bondarenko D., Musharavati F., and Pokharel S., 2014. Comfort-based fuzzy control optimization for energy conservation in HVAC systems. Control Engineering Practice 32: 172–182.

Homod R., Sahari K.S.M., Almurib H.A., and Nagi F.H., 2012. Gradient auto-tuned Takagi–Sugeno fuzzy forward control of a HVAC system using predicted mean vote index. Energy and Buildings 49: 254–267.

Attaran S.M., Yusof R., and Selamat H., 2016. A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system. Applied Thermal Engineering 99: 613–624.

Wemhoff A.P., 2012. Calibration of HVAC equipment PID coefficients for energy conservation. Energy and Buildings 45: 60–66.

Reynoso-Meza G., Blasco X., Sanchis J., and Martínez M., 2014. Controller tuning using evolutionary multi-objective optimisation: Current trends and applications. Control Engineering Practice 28: 58–73.

Zajic I., Larkowski T. Burnham K.J., and Hill D., 2012. Control analysis and tuning of an industrial temperature control system. IFAC Proceedings Volumes 45(3): 679–684.

Soyguder S., Karakose M., and Alli H., 2009. Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system. Expert Systems with Applications 36(3): 4566–4573.

Moradi H., Setayesh H., and Alasty A., 2016. PID-Fuzzy control of air handling units in the presence of uncertainty. International Journal of Thermal Sciences 109: 123–135.

Homod R., 2018. Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings. Renewable Energy 126: 49–64.

Attaran S.M., Yusof R., and Selamat H., 2016. A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system. Applied Thermal Engineering 99: 613–624.

Guo C., Song Q., and Cai W., 2007. A neural network assisted cascade control system for air handling unit. IEEE Transactions on Industrial Electronics 54(1): 620–628.

Wei L. and Z. Junmin. 2012. Particle swarm optimization PID neural network control method in the main steam temperature control system. In 2012 International Conference on Computer Science and Electronics Engineering, Hangzhou, China. pp. 137–140.

Katić K., Li R., Verhaart J., and Zeiler W., 2018. Neural network based predictive control of personalized heating systems. Energy and Buildings 174: 199–213.

Ruiz P.A., De La Flor F.S., Felix J.M., Lissén J.S., and Martín J.G., 2016. Applying the HVAC systems in an integrated optimization method for residential building's design. A case study in Spain. Energy and Buildings 119: 74–84.

Kerdan I.G., Raslan R., and Ruyssevelt P., 2016. An exergy-based multi-objective optimisation model for energy retrofit strategies in non-domestic buildings. Energy 117(2): 506–522.

Wright J. and A. Alajmi. 2016. Efficient genetic algorithm sets for optimizing constrained building design problem. International Journal of Sustainable Built Environment 5(1): 123–131.

Wetter M., 2000. Design optimization with GenOpt. Building Energy Simulation User News 21: 19–28.

Lai K., Wang W., and Giles H., 2014. Performance analysis of an energy efficient building prototype by using TRNSYS.

Arcangeli G., 2008. Advanced tools for building simulation: Energy and airflow.

Shi X., Tian Z., Chen W., Si B., and Jin X., 2016. A review on building energy efficient design optimization from the perspective of architects. Renewable and Sustainable Energy Reviews 65: 872–884.

Yigit S. and B. Ozorhon. 2018. A simulation-based optimization method for designing energy efficient buildings. Energy and Buildings 178: 216–227.

Gou S., Nik V.M., Scartezzini J.L., Zhao Q., and Li Z., 2018. Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy and Buildings 169: 484–506.

Ruiz P.A, Martín J.G., Lissén J.M.S., and de la Flor F.J., 2014. An integrated optimisation method for residential building design: A case study in Spain. Energy and Buildings 80: 158–168.

Kerdan I.G., Raslan R., Ruyssevelt P., and Gálvez D.M., 2016. An exergoeconomic-based parametric study to examine the effects of active and passive energy retrofit strategies for buildings. Energy and Buildings 133: 155–171.