Neural predictive control for single-speed ground source heat pumps connected to a floor heating system for typical French dwelling

Building Services Engineering Research and Technology

Volumn 35, Issue 2, 2014, Pages 182-197

  Salque, T ^a,b   Marchio, D ^b   Riederer, P ^a  

^a Centre Scientifique et Technique du Bâtiment ^*  (France)

^b PSL RESEARCH UNIVERSITY   (France)

Author keywords

artificial neural networks; energy savings; floor heating; Ground source heat pump; predictive control

Indexed keywords

CONVENTIONAL CONTROLLERS; EXTERNAL DISTURBANCES; FLOOR HEATING; FLOOR HEATING SYSTEMS; NEURAL PREDICTIVE CONTROLLERS; NEURAL-PREDICTIVE CONTROLS; PREDICTIVE CONTROL; PREDICTIVE CONTROLLER;

CONTROLLERS; ENERGY CONSERVATION; ENERGY UTILIZATION; HEATING EQUIPMENT; HOUSING; NEURAL NETWORKS;

GEOTHERMAL HEAT PUMPS;

EID: 84887220230     PISSN: 01436244     EISSN: 14770849     Source Type: Journal    
DOI: 10.1177/0143624413480370     Document Type: Article

References (29)

1. Chen TY. Application of adaptive predictive control to a floor heating system with a large thermal lag. Energy Building. 2002 ; 34 (1). 45-51
2. Boait PJ, Fan D, Stafford A. Performance and control of domestic ground-source heat pumps in retrofit installations. Energy Building. 2011 ; 43 (8). 1968-1976
3. Parent P Optimal control theory applied to dwelling heating system. IRCOSE: Agence française pour la maitrise de l'énergie ; 1987 :
4. Hartman TB. Dynamic control: Fundamentals and considerations. ASHREA Transact 4. 1988 ; 1: 599-609
5. Nygard AM Predictive thermal control of building systems. PhD thesis Ecole polytechnique fédérale de Lausanne ; 1990 :
6. Lute P, van Paassen D. Optimal indoor temperature control using a predictor. IEEE Control Systems. 1995 ; 12: 4-9
7. Curtiss PS, Shavit G, Kreider K. Neural networks applied to buildings - a tutorial and case studies in prediction and adaptive control. ASHRAE Transact. 1996 ; 102 ( 1 ):
8. Morel N, Bauer M, El-Khoury, et al. Neurobat, a predictive and adaptive heating control system using artificial neural networks. Int J Solar Energy. 2000 ; 21: 161-201
9. Karlsson H, Hagentoft C-E. Application of model based predictive control for water-based floor heating in low energy residential buildings. Building Environ. 2011 ; 46 (3). 556-569
10. Verhelst C, Logist F, Van Impe J, et al. Study of the optimal control problem formulation for modulating air-to-water heat pumps connected to a residential floor heating system. Energy Building. 2012 ; 45: 43-53
11. Argiriou AA, Bellas-Velidis I, Kummert M, et al. A neural network controller for hydronic heating systems of solar buildings. Neural Network. 2004 ; 17 (3). 427-440
12. Tödtli J In: The Second World Congress on Heating, Ventilating, Refrigerating and Air-Conditioning. Clima 2000, Sarajevo, Yugoslavia ; 1989: 329-336.
13. Elkhuizen PA, Peitsman HC, Wienk WJ. A new design guideline for the heating and cooling curve in AHU units of HVAC systems. Building Serv Eng Res Technol. 2003 ; 24 (3). 191-202
14. Fulcheri L, Neirac FP, Mouel AL, et al. Chaufage des bâtiments: intermittence et lois de régulation en boucle ouverte. Revue générale de thermique. 1994 ; 374: 77-91
15. Yang I-H, Yeo M-S, Kim K-W. Application of artificial neural network to predict the optimal start time for heating system in building. Energy Conver Manage. 2003 ; 44 (17). 2791-2809
16. Prívara S, Siroký J, Ferkl L, et al. Model predictive control of a building heating system: The first experience. Energy Building. 2010 ; 43 (2-3). 564-572
17. Aström KJ, Wittenmark B Computer controlled systems: theory and design. Mainland China: Prentice Hall ; 1990 :
18. Philippe M, Bernier M, Marchio D. Sizing calculation spreadsheet vertical geothermal borefields. ASHRAE J. 2010 ; 52 (7). 20-28
19. Svozil D, Kvasnicka V, Pospichal Jí. Introduction to multi-layer feed-forward neural networks. Chemometr Intelligent Lab Sys. 1997 ; 39 (1). 43-62
20. MATLAB. Version 7.0.1, (R14SP1). Mathworks Inc., Ma., USA, http://wwwmathworkscom.
21. Tang QY, Feng MG DPS data processing system for practical statistics. Beijing: Science Press ; 2002: 648-648.
22. Chernichow T, Piras A, Imhof K, et al. Short term electric load forecasting with artificial neural networks. Eng Intelligent Syst. 1996 ; 2: 85-99
23. Sfetsos A, Coonick AH. Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy. 2000 ; 68 (2). 169-178
24. Marquez R, Coimbra CFM. Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Solar Energy. 2011 ; 85 (5). 746-756
25. Khotanzad A, Davis MH, Abaye A, et al. An artificial neural network hourly temperature forecaster with applications in load forecasting. IEEE Trans Power Syst. 1996 ; 11 (2). 870-876
26. Tasadduq I, Rehman S, Bubshait K. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renew Energy. 2002 ; 25 (4). 545-554
27. Perez R, Ineichen P, Seals R, et al. Modeling daylight availability and irradiance components from direct and global irradiance. Solar Energy. 1990 ; 44: 271-289
28. Klein SA, Duffie JA, Mitchell JC, et al TRNSYS - a Transient Simulation Program user's manual. Madison: University of Wisconsin ; 1994 :
29. Partenay V, Riederer P, Salque T, et al. The influence of the borehole short-time response on ground source heat pump system efficiency. Energy Building. 2011 ; 43 (6). 1280-1287