Hybrid Adaptive Modeling to Enhance Robustness of Real-Time Optimization
Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129215Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 International energy outlet (IEO) energy information administration. Available at
 U.S. DOE 3.1.4 2010, Commercial Energy End-Use Splits, by Fuel Type (Quadrillion Btu), Building Energy Data Book.
 Hong Kong Electrical Consumption, Hong Kong energy statistics report, Census and Statistics department, Hong Kong Government, Hong Kong, 2015. Available at
 Seem, J.E., Park, C. and House, J.M., 1999. A new sequencing control strategy for air-handling units. HVAC&R Research, 5(1), pp.35-58.
 Henze, G.P., Dodier, R.H. and Krarti, M., 1997. Development of a predictive optimal controller for thermal energy storage systems. HVAC&R Research, 3(3), pp.233-264.
 Nassif, N., Kajl, S. and Sabourin, R., 2005. Optimization of HVAC control system strategy using two-objective Genetic algorithm. HVAC&R Research, 11(3), pp.459-486.
 Kusiak, A., Xu, G. and Zhang, Z., 2014. Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method. Energy Conversion and Management, 85, pp.146-153.
 Cho, H., Smith, A.D. and Mago, P., 2014. Combined cooling, heating and power: a review of performance improvement and optimization. Applied Energy, 136, pp.168-185.
 D.R. Clark, in U.S.D. Commerce, N.B.o. Standarts, N.E.o. Laboratory, C. Technology, B.E,f.B, Division (Eds.), 1985. HVACSIM+ Building Systems and Equipment Simulation Program Reference Manual. Gaithersburg, MD 20899.
 J.P. Bourdouxhe, M. Groodent, J. LeBrun, 1998. Reference Guide for Dynamic Models of HVAC Equipment: American Society of Heating, Refrigerating and Air Conditioning Engineers Incorporated.
 Asad H. S., Yuen R. K. K., Huang, G., 2016. Degree of Freedom based Set-Point Reset Scheme for HVAC Real-Time Optimization. Energy and buildings.
 Pengfei G., Xuezhi W., Yingshi H., 2010. The Enhanced Genetic Algorithms for Optimization Design, 3rd International Conference on Biomedical Engineering and Informatics, IEEE.
 Randy L. H., Sue E. H., 2004. Practical Genetic Algorithms, second edition, Willy Interscience, a John Willy and sons , INC., Publications, pp. 18-24.
 Y. Sun, G. Huang, Z. Li, S. Wang, (2013). Multiplexed optimization for complex air conditioning systems, Building and Environment, Volume 65, Pages 99-108