ARCHIVES
Original Article
Model predictive control of indoor thermal environment using building thermal mass as renewable energy source
Gum Ryong Pak1
Un Jin Pak*2
1 2 Faculty of Automation Engineering, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea.
Published Online: September-October 2025
Pages: 130-137
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250505021
References
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2. Olesen BW. Radiant floor heating in theory and practice. ASHRAE journal. 2002; 44: 19.
3. Xingji Yu, Zhengru Ren, Peng Liu, Lars Imsland, Laurent Georges. Comparison of time-invariant and adaptive linear grey-box models for model predictive control of residential buildings. Building and Environment. 2024; 254: 111391. https://doi.org/10.1016/j.buildenv.2024.111391
4. Sattari S, Farhanieh B. A parametric study on radiant floor heating system performance. Renewable Energy. 2006; 31: 1617-1626.
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6. Zhou G, He J. Thermal performance of a radiant floor heating system with different heat storage materials and heating pipes. Applied Energy. 2015; 138: 648-660.
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8. Weitzmann P. Kragh J, Roots P, Svendsen S. Modelling floor heating systems using a validated two-dimensional ground-coupled numerical model. Building and Environment. 2005; 40: 153-163.
9. Laouadi A. Development of a radiant heating and cooling model for building energy simulation software. Building and Environment. 2004; 39: 421-431.
10. Maomao Hu, Fu Xiao, John Bagterp Jørgensen, Rongling Li. Price-responsive model predictive control of floor heating systems for demand response using building thermal mass. Applied Thermal Engineering. 2019. https://doi.org/10.1016/j.applthermaleng.2019.02.107
11. Chen T. Application of adaptive predictive control to a floor heating system with a large thermal lag. Energy and Buildings. 2002; 34: 45-51.
12. Weitzmann P. Modelling building integrated heating and cooling systems: Technical University of Denmark; 2004.
13. L. Pérez-Lombard, J. Ortiz, C. Pout. A review on buildings energy consumption information, Energy Build. 2008; 34 :394–398.
14. H. Harb, N. Boyanov, L. Hernandez, R. Streblow, D. Müller. Development and validation of grey-box models for forecasting the thermal response of occupied buildings, Energy Build. 2016; 117: 199–207. https://doi.org/10.1016/j. enbuild.2016.02.021.
15. J. Le Dréau, P. Heiselberg. Energy flexibility of residential buildings using short term heat storage in the thermal mass, Energy. 2016; 111: 991–1002. https://doi.org/10.1016/j.energy.2016.05.076.
16. G. Reynders. Quantifying the impact of building design on the potential of structural storage for active demand response in residential buildings, 2015. https://doi.org/10.13140/RG.2.1.3630.2805.
17. Xingji Yu, Laurent Georges, Lars Imsland. Data pre-processing and optimization techniques for statistic and deterministic low-order grey-box models of residential buildings, Energy & Buildings. 2021; 236: 110775-110791. https://doi.org/10.1016/j.enbuild.2021.110775
18. C.R. Cutler, B.L. Ramaker. Dynamic matrix control – a computer control algorithm, in: Proceedings of the 1980 Joint Automatic Control Conference, San Francisco, Aug. 13-15, 1980.
19. S.J. Qin, T.A. Badgwel. A survey of industrial model predictive control technology, Control Engineering Practice. 2003; 7: 733-764.
20. X. Li, J. Wen. Building energy consumption on-line forecasting using physics based system identification, Energy and Buildings. 2014; 82: 1-12.
21. B.C. Kuo, F. Golnaraghi. Automatic control systems, 8th Edition, John Wiley & Sons, Inc.2003.
22. Cigler J, Gyalistras D, Široky J, Tiet V, Ferkl L. Beyond theory: the challenge of implementing model predictive control in buildings. Proceedings of 11th Rehva world congress, Clima2013.
23. Killian M, Zauner M, Kozek M. Comprehensive smart home energy management system using mixed-integer quadratic-programming. Applied Energy. 2018;222:662-72.
24. The MathWorks. System identification toolbox. https://www.mathworks.com/products/sysid.html.
25. Air-conditioning Heating & Refrigeration Institute. 2015 Standard for Performance Rating of Positive Displacement Refrigerant Compressors and Compressor Units. Arlington, VA, USA2015.
26. Zhou B, Li W, Chan KW, Cao Y, Kuang Y, Liu X, et al. Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews. 2016;61:30-40.
27. Nord Pool. Day-ahead Prices in Denmark. URL https://wwwnordpoolgroupcom/Market-data1/Dayahead/Area-Prices/DK/Hourly/?dd=DK2&view=table. 2018.
28. Sultana WR, Sahoo SK, Sukchai S, Yamuna S, Venkatesh D. A review on state of art development of model predictive control for renewable energy applications. Renewable and Sustainable Energy Reviews. 2017;76:391-406.
29. Halvgaard R, Poulsen NK, Madsen H, Jørgensen JB. Economic model predictive control for building climate control in a smart grid. Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES: IEEE; 2012: 1-6.
30. Löfberg J. YALMIP: A toolbox for modeling and optimization in MATLAB. Computer Aided Control Systems Design, 2004 IEEE International Symposium on: IEEE; 2004: 284-289.
31. Gurobi Optimization I. Gurobi optimizer reference manual. 2016. http://www.gurobi.com.
32. Hu M, Xiao F, Wang L. Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model. Applied Energy. 2017;207:324-335.
2. Olesen BW. Radiant floor heating in theory and practice. ASHRAE journal. 2002; 44: 19.
3. Xingji Yu, Zhengru Ren, Peng Liu, Lars Imsland, Laurent Georges. Comparison of time-invariant and adaptive linear grey-box models for model predictive control of residential buildings. Building and Environment. 2024; 254: 111391. https://doi.org/10.1016/j.buildenv.2024.111391
4. Sattari S, Farhanieh B. A parametric study on radiant floor heating system performance. Renewable Energy. 2006; 31: 1617-1626.
5. Bozkır O, Canbazoğlu S. Unsteady thermal performance analysis of a room with serial and parallel duct radiant floor heating system using hot airflow. Energy and Buildings. 2004; 36: 579-586.
6. Zhou G, He J. Thermal performance of a radiant floor heating system with different heat storage materials and heating pipes. Applied Energy. 2015; 138: 648-660.
7. Xia Y, Zhang X-S. Experimental research on a double-layer radiant floor system with phase change material under heating mode. Applied Thermal Engineering. 2016; 96: 600-606.
8. Weitzmann P. Kragh J, Roots P, Svendsen S. Modelling floor heating systems using a validated two-dimensional ground-coupled numerical model. Building and Environment. 2005; 40: 153-163.
9. Laouadi A. Development of a radiant heating and cooling model for building energy simulation software. Building and Environment. 2004; 39: 421-431.
10. Maomao Hu, Fu Xiao, John Bagterp Jørgensen, Rongling Li. Price-responsive model predictive control of floor heating systems for demand response using building thermal mass. Applied Thermal Engineering. 2019. https://doi.org/10.1016/j.applthermaleng.2019.02.107
11. Chen T. Application of adaptive predictive control to a floor heating system with a large thermal lag. Energy and Buildings. 2002; 34: 45-51.
12. Weitzmann P. Modelling building integrated heating and cooling systems: Technical University of Denmark; 2004.
13. L. Pérez-Lombard, J. Ortiz, C. Pout. A review on buildings energy consumption information, Energy Build. 2008; 34 :394–398.
14. H. Harb, N. Boyanov, L. Hernandez, R. Streblow, D. Müller. Development and validation of grey-box models for forecasting the thermal response of occupied buildings, Energy Build. 2016; 117: 199–207. https://doi.org/10.1016/j. enbuild.2016.02.021.
15. J. Le Dréau, P. Heiselberg. Energy flexibility of residential buildings using short term heat storage in the thermal mass, Energy. 2016; 111: 991–1002. https://doi.org/10.1016/j.energy.2016.05.076.
16. G. Reynders. Quantifying the impact of building design on the potential of structural storage for active demand response in residential buildings, 2015. https://doi.org/10.13140/RG.2.1.3630.2805.
17. Xingji Yu, Laurent Georges, Lars Imsland. Data pre-processing and optimization techniques for statistic and deterministic low-order grey-box models of residential buildings, Energy & Buildings. 2021; 236: 110775-110791. https://doi.org/10.1016/j.enbuild.2021.110775
18. C.R. Cutler, B.L. Ramaker. Dynamic matrix control – a computer control algorithm, in: Proceedings of the 1980 Joint Automatic Control Conference, San Francisco, Aug. 13-15, 1980.
19. S.J. Qin, T.A. Badgwel. A survey of industrial model predictive control technology, Control Engineering Practice. 2003; 7: 733-764.
20. X. Li, J. Wen. Building energy consumption on-line forecasting using physics based system identification, Energy and Buildings. 2014; 82: 1-12.
21. B.C. Kuo, F. Golnaraghi. Automatic control systems, 8th Edition, John Wiley & Sons, Inc.2003.
22. Cigler J, Gyalistras D, Široky J, Tiet V, Ferkl L. Beyond theory: the challenge of implementing model predictive control in buildings. Proceedings of 11th Rehva world congress, Clima2013.
23. Killian M, Zauner M, Kozek M. Comprehensive smart home energy management system using mixed-integer quadratic-programming. Applied Energy. 2018;222:662-72.
24. The MathWorks. System identification toolbox. https://www.mathworks.com/products/sysid.html.
25. Air-conditioning Heating & Refrigeration Institute. 2015 Standard for Performance Rating of Positive Displacement Refrigerant Compressors and Compressor Units. Arlington, VA, USA2015.
26. Zhou B, Li W, Chan KW, Cao Y, Kuang Y, Liu X, et al. Smart home energy management systems: Concept, configurations, and scheduling strategies. Renewable and Sustainable Energy Reviews. 2016;61:30-40.
27. Nord Pool. Day-ahead Prices in Denmark. URL https://wwwnordpoolgroupcom/Market-data1/Dayahead/Area-Prices/DK/Hourly/?dd=DK2&view=table. 2018.
28. Sultana WR, Sahoo SK, Sukchai S, Yamuna S, Venkatesh D. A review on state of art development of model predictive control for renewable energy applications. Renewable and Sustainable Energy Reviews. 2017;76:391-406.
29. Halvgaard R, Poulsen NK, Madsen H, Jørgensen JB. Economic model predictive control for building climate control in a smart grid. Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES: IEEE; 2012: 1-6.
30. Löfberg J. YALMIP: A toolbox for modeling and optimization in MATLAB. Computer Aided Control Systems Design, 2004 IEEE International Symposium on: IEEE; 2004: 284-289.
31. Gurobi Optimization I. Gurobi optimizer reference manual. 2016. http://www.gurobi.com.
32. Hu M, Xiao F, Wang L. Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model. Applied Energy. 2017;207:324-335.
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