Adaptive HVAC zone modeling for sustainable buildings

Energy and Buildings

Volumn 42, Issue 4, 2010, Pages 412-421

  Platt, Glenn ^a   Li, Jiaming ^a   Li, Ronxin ^a   Poulton, Geoff ^a   James, Geoff ^a   Wall, Josh ^a  

^a CSIRO   (Australia)

Author keywords

Genetic algorithms; HVAC control; HVAC zone modeling; Kalman filtering

Indexed keywords

ACCURATE PREDICTION; ACTIVE AREA; BUILDING ENERGY CONSUMPTION; BUILDING ZONES; ENERGY FLOW; ENVIRONMENTAL CONDITIONS; EXPERIMENTAL MEASUREMENTS; HVAC CONTROL; HVAC SYSTEM; IT SERVICES; KALMAN-FILTERING; OPTIMAL PERFORMANCE; PHYSICAL PRINCIPLES; PREDICTION ACCURACY; PREDICTION TECHNIQUES; SUSTAINABLE BUILDING; ZONE MODEL; ZONE MODELING;

ELECTRIC LOAD FORECASTING; ENERGY CONSERVATION; GENETIC ALGORITHMS; GENETIC ENGINEERING; HEATING; INTELLIGENT BUILDINGS; KALMAN FILTERS; OPTIMIZATION;

AIR CONDITIONING;

EID: 77649193488     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2009.10.009     Document Type: Article

References (24)

1. Hudson G., and Underwood C.P. A simple building modeling procedure for MATLAB/SIMULINK. Proceedings of the International Building Performance and Simulation Conference. Kyoto (1999)
2. Tor K., and Ritter F. Using a genetic algorithm to optimize the fit of cognitive models. Proceedings of the International Conference on Cognitive Modeling (2004) 308-313
3. Levermore G.J. Building Energy Management Systems (1992), E & FN Spon, London
4. Levermore G.J. Simple model for an optimiser. Building Services Engineering Research & Technology 9 3 (1988) 109-116
5. Tashtoush B., Molhim M., and Rousan M. Dynamic model of an HVAC system for control analysis. Energy 30 (2005) 1729-1745
6. Karatasou S., Santamouris M., and Geros V. Modeling and predicting building's energy use with artificial neural networks: methods and results. Journal of Energy and Buildings 38 (2006) 949-958
7. Dong B., Cao C., and Lee S. Applying support vector machines to predict building energy consumption in tropical region. Journal of Energy and Buildings 37 (2005) 545-553
8. Wang S.W., and Xu X.H. Simplified building model for transient thermal performance estimation using GA-based parameter identification. International Journal of Thermal Science 45 4 (2006) 419-432
9. Wang S.W., and Xu X.H. Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm. Energy Conversion and Management 47 13-14 (2006) 1927-1941
10. Wang S.W. Dynamic simulation of building VAV air-conditioning system and evaluation of EMCS on-line control strategies. Building and Environment 34 6 (1999) 681-705
11. Park C.-S., Augenbroe G., Messadi T., Thitisawat M., and Sadegh N. Calibration of a lumped simulation model for double-skin façade systems. Energy and Buildings 36 (2004) 1117-1130
12. Djuric N., Novakovic V., and Frydenlund F. Heating system performance estimation using optimization tool and BEMS data. Energy and Buildings 40 (2008) 1367-1376
13. National Electricity Market Management Company (NEMMCO), Australia, 2007. http://www.nemmco.com.au/, accessed December 2008.
14. G. Welch, G. Bishop, An Introduction to the Kalman Filter, Technique Report Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3175, 2006.
15. Ashley F., and Emery. Estimating parameters and refining thermal models by using the extended Kalman filter approach. Journal Heat Transfer 126 5 (2004) 809-817
16. Saeid Habibi. Parameter estimation using a combined variable structure and Kalman filtering approach. Journal of Dynamic Systems, Measurement, and Control 130 September (5) (2008) 051004 (14 pages)
17. I.F. MacGill, Optimising decentralised power system operation using dual evolutionary programming, Ph.D. Thesis, UNSW, Australia, 1998.
18. Guo Y., Li J., and James G. Evolutionary optimisation of distributed energy resources. The 18th Australian Joint Conference on Artificial Intelligence (AI'05). Sydney, Australia, December 5-9 (2005) 1086-1091
19. Li J., Poulton G., and James G. Agent-based distributed energy management. 20th Australian Joint Conference on Artificial Intelligence (AI'07). Gold Coast, Queensland, Australia, December 2-6 (2007) 569-578
20. Li R., Li J., Poulton G., and James G. Agent-based optimisation systems for electrical load management. Proc. First International Workshop on Optimisation in Multi-Agent Systems (OPTMAS). Estoril, Portugal (2008) 60-69 in pewaa May
21. Li J., Poulton G., James G., and Guo Y. Multiple energy resource agent coordination based on electricity price. Journal of Distributed Energy Resources 5 April-June (2) (2009) 103-120
22. Li J., James G., and Poulton G. Set-points based optimal multi-agent coordination for controlling distributed energy loads. 3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems. San Francisco, California, September 14-18 (2009)
23. Tully F.R., and Kaye R.J. Unit commitment in competitive electricity markets using genetic algorithms. Trans. of the Institute of Electrical Engineers of Japan 117-B 6 (1997) 815-821
24. J.P. Rennard, Introduction to genetic algorithms, PhD thesis, 2000.