Comparison and Simulation of Building Thermal Models for Effective Energy Management
Affiliation(s)
1 Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, Canada. 2 Department of Mechanical Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, Canada.
1 Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, Canada. 2 Department of Mechanical Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, Canada.
ABSTRACT
Energy consumption reduction efforts in the residential buildings sector represent socio-economical, technological and environmental preoccupations which justify advanced scientific research. These lead to use inverse models to describe thermal behavior and to evaluate the energy consumption of buildings. Their principal goal is to provide supporting evidence of enhanced energy performances and predictions. More specifically, research questions are related to building thermal modeling which is the most appropriate in a smart grid context. In this context, the models are reviewed according to three categories. The first category is based on physical and basic principle modeling (white-box). The second offers a much simpler structure which is the statistical models (black-box). The black-box is used for prediction of energy consumption and heating/ cooling demands. Finally, the third category is a hybrid method (grey-box), which uses both physical and statistical modeling techniques. In this paper, we propose a detailed review and simulation of the main thermal building models. Our comparison and simulation results demonstrate that the grey-box is the most effective model for management of buildings energy consumption.
Energy consumption reduction efforts in the residential buildings sector represent socio-economical, technological and environmental preoccupations which justify advanced scientific research. These lead to use inverse models to describe thermal behavior and to evaluate the energy consumption of buildings. Their principal goal is to provide supporting evidence of enhanced energy performances and predictions. More specifically, research questions are related to building thermal modeling which is the most appropriate in a smart grid context. In this context, the models are reviewed according to three categories. The first category is based on physical and basic principle modeling (white-box). The second offers a much simpler structure which is the statistical models (black-box). The black-box is used for prediction of energy consumption and heating/ cooling demands. Finally, the third category is a hybrid method (grey-box), which uses both physical and statistical modeling techniques. In this paper, we propose a detailed review and simulation of the main thermal building models. Our comparison and simulation results demonstrate that the grey-box is the most effective model for management of buildings energy consumption.
KEYWORDS
Building Control, Inverse Modeling, Building Prediction Model, Simplified Building Models, Parameters Estimation
Building Control, Inverse Modeling, Building Prediction Model, Simplified Building Models, Parameters Estimation
Cite this paper
Amara, F. , Agbossou, K. , Cardenas, A. , Dubé, Y. and Kelouwani, S. (2015) Comparison and Simulation of Building Thermal Models for Effective Energy Management. Smart Grid and Renewable Energy, 6, 95-112. doi: 10.4236/sgre.2015.64009.
Amara, F. , Agbossou, K. , Cardenas, A. , Dubé, Y. and Kelouwani, S. (2015) Comparison and Simulation of Building Thermal Models for Effective Energy Management. Smart Grid and Renewable Energy, 6, 95-112. doi: 10.4236/sgre.2015.64009.
References
[1] Halvgaard, R., Poulsen, N.K., Madsen, H. and JØrgensen, J.B. (2012) Economic Model Predictive Control for Building Climate Control in a Smart Grid. 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington DC, 16-20 January 2012, 1-6.
http://dx.doi.org/10.1109/ISGT.2012.6175631 [2] Haghighi, M.M. (2013) Controlling Energy-Efficient Buildings in the Context of Smart Grid: A Cyber Physical System Approach. Ph.D Dissertation, University of California at Berkeley, Berkeley. [3] Borlase, S., Ed. (2012) Smart Grids: Infrastructure, Technology, and Solutions. CRC Press, Boca Raton. [4] Zhou, Q., Wang, S.W., Xu, X.H. and Xiao, F. (2008) A Grey-Box Model of Next-Day Building Thermal Load Prediction for Energy-Efficient Control. International Journal of Energy Research, 32, 1418-1431.
http://dx.doi.org/10.1002/er.1458 [5] Crabb, J.A, Murdoch, N. and Penman, J.M. (1987) A Simplified Thermal Response Model. Building Service Engineering Research and Technology, 8, 13-19.
http://dx.doi.org/10.1177/014362448700800104 [6] Katipamula, S. and Brambley, M. (2005) Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part II. HVAC&R Research, 11, 169-187.
http://dx.doi.org/10.1080/10789669.2005.10391133 [7] Yang, Z.Y., Li, X.L., Tang, K., Yao, X., Bowers, C.P. and Schnier, T. (2012) An Efficient Evolutionary Approach to Parameter Identification in a Building Thermal Model. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42, 957-969. [8] Energy Solution Centre (2011) Easy$ Tip Sheets—Energy Advice Saving Yukoners Money. Energy Solution Centre Report, Whitehorse, 1-4. www.esc.gov.yk.ca [9] Pouresmaeil, E., Gonzalez, J.M., Bhattacharya, K. and Canizares, C.A. (2013) Development of a Smart Residential Load Simulator for Energy Management in Smart Grids. IEEE Transactions on Power Systems, 1-8. [10] Park, H., Ruellan, M., Bouvet, A., Monmasson, E. and Bennacer, R. (2011) Thermal parameter identification of simplified building model with electric appliance. 11th International Conference on Electrical Power Quality and Utilisation (EPQU), Lisbon, 17-19 October 2011, 1-6.
http://dx.doi.org/10.1109/EPQU.2011.6128822 [11] Jiménez, M.J. and Heras, M.R. (2009) Application of Different Dynamic Analysis Approaches to Estimate the U and G Values of Building Components. Building and Environment, 44, 361-367. [12] Merabtine, A. (2012) Modélisation Bond Graphs en vue de l’Efficacité énergétique du Batiment. Thesis, Université de Lorraine, Lorraine. [13] Zayane, C. (2011) Identification d’un modèle de comportement thermique de batiment à partir de sa courbe de charge. ParisTech, Paris. [14] Brause, R. (2010) Adaptive Modellierung und Simulation. Rüdiger Brause, Ed., Frankfurt. [15] Khan, M.E. and Farmeena, K. (2012) A Comparative Study of White Box, Black Box and Grey Box Testing Techniques. International Journal of Advanced Computer Science and Applications, 3, 12-15. [16] Berthou, T., Stabat, P., Salvazet, R. and Marchio, D. (2012) Comparaison de modèles linéaires inverses pour la mise en place de stratégies d’ effacement. Rencontres AUGC-IBPSA, 1-12. [17] Kawashima, M., Dorgan, C.E. and Mitchell, J.W. (1995) Hourly Thermal Load Prediction for the Next 24 Hours by ARIMA, EWMA, LR, and an Artificial Neural Network. ASHRAE Transactions, 101, 186. [18] Stevenson, W.J. (1994) Predicting Building Energy Parameters Using Artificial Neural Nets. Transactions of the American Society of Heating, Refrigerating and Airconditioning Engineers, 100, 1081-1087. [19] Ohlsson, M., Petersson, C., Pi, H., RÖgnvaldsson, T. and SÖderberg, B. (1994) Predicting System Loads with Artificial Neural Networks—Methods and Results from “The Great Energy Predictor Shootout”. ASHRAE Transactions, 100, 1063-1074. [20] Feuston, B.P. and Thurtell, J.H. (1994) Generalized Nonlinear Regression with Ensemble of Neural Nets: The Great Energy Predictor Shootout. ASHRAE Transactions, 100, 1075-1080. [21] Iijima, M., Takagi, K., Takeuchi, R. and Matsumoto, T. (1994) A Piecewise-Linear Regression on the ASHRAE Time-Series Data. ASHRAE Transactions, 100, 1088-1095. [22] Chen, C., Wang, J., Member, S., Heo, Y. and Kishore, S. (2013) MPC-Based Appliance Scheduling for Residential Building Energy Management Controller. IEEE Transactions on Smart Grid, 4, 1401-1410. [23] Szikra, C. (2014) Calculation of Heat Loss for Residential Buildings. [24] Singh, R. and Vyakaranam, B. (2012) Evaluation of Representative Smart Grid Investment Grant Project Technologies: Distributed Generation. PNNL, Richland.
http://www.esc.gov.yk.ca/ [25] Maasoumy, M., Moridian, B., Meysam, R. and Mahdi, S. (2013) Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control. Proceedings of the ASME Dynamic Systems and Control Conference, Palo Alto, 21-23 October 2013, 1-10. [26] Horváth, G. (2002) Neural Networks in System Modeling. In: Ablameyko, S., Goras, L., Gori, M. and Piuri, V., Eds., Neural Networks in Measurement Systems, IOS Press, Amsterdam, 43-78. [27] Verhelst, C. (2012) Model Predictive Control of Ground Coupled Heat Pump Systems for Office Buildings. Katholieke University Leuven, Leuven. [28] Christian, N., Dirk, J., Burhenne, S. and Florita, A. (2011) Modellbasierte Methoden für die Fehlererkennung und Optimierung im Gebäudebetrieb. Fraunhofer ISE, Technical Report 0327410A-C, 1-276. [29] Lebrun, J. (2001) Simulation of a HVAC System with the Help of an Engineering Equation Solver Plant of an Engineering Equation Solver. Proceedings of the 7th International IBPSA Conference, Rio de Janeiro, 13-15 August 2001, 1119-1126. [30] Deque, F., Ollivier, F. and Poblador, A. (2000) Grey Boxes Used to Represent Buildings with a Minimum Number of Geometric and Thermal Parameters. Energy and Buildings, 31, 29-35. [31] Verhelst, C., Logist, F., Van Impe, J. and Helsen, L. (2012) Study of the Optimal Control Problem Formulation for Modulating Air-to-Water Heat Pumps Connected to a Residential Floor Heating System. Energy and Buildings, 45, 43-53.
http://dx.doi.org/10.1016/j.enbuild.2011.10.015 [32] Ljung, L. (2009) System Identification: Theory for the User. Prentice-Hall, Upper Saddle River. [33] Tulleken, H.J.A.F. (1991) Application of the Grey-Box Approach to Parameter Estimation in Physicochemical Models. Proceedings of the 30th IEEE Conference on Decision and Control, Brighton, 11-13 December 1991, 1177-1183. [34] Clarke, J.A., Cockroft, J., Conner, S., Hand, J.W., Kelly, N.J., Moore, R., O’Brien, T. and Strachan, P. (2002) Simulation-Assisted Control in Building Energy Management Systems. Energy and Buildings, 34, 933-940.
http://dx.doi.org/10.1016/S0378-7788(02)00068-3 [35] Costanzo, G.T., Sossan, F., Marinelli, M., Bacher, P. and Madsen, H. (2013) Grey-Box Modeling for System Identification of Household Refrigerators: A Step toward Smart Appliances. Proceedings of the 4th International Youth Conference on Energy (IYCE), Siofok, 6-8 June 2013, 1-5. [36] Yudong, M. (2012) Model Predictive Control for Energy Efficient Buildings. University of California, Berkeley. [37] Deng, K. and Goyal, S. (2014) Structure-Preserving Model Reduction of Nonlinear Building Thermal Models. Automatica, 50, 1188-1195.
http://dx.doi.org/10.1016/j.automatica.2014.02.009 [38] Shariatzadeh, F. and Srivastava, A.K. (2013) Look-Ahead Control Approach for Thermostatic Electric Load in Distribution System. Proceedings of the 2013 North American Power Symposium (NAPS), Manhattan, 22-24 September 2013, 1-6.
http://dx.doi.org/10.1109/NAPS.2013.6666878 [39] Braun, J.E. (1990) Reducing Energy Costs and Peak Electrical Demand through Optimal Control of Building Thermal Storage. ASHRAE Transactions, 96, 876-888. [40] Siroký, J., Oldewurtel, F., Cigler, J. and Prívara, S. (2011) Experimental Analysis of Model Predictive Control for an Energy Efficient Building Heating System. Applied Energy, 88, 3079-3087.
http://dx.doi.org/10.1016/j.apenergy.2011.03.009 [41] Verhaegen, M. and Verdult, V. (2012) Filtering and System Identification: A Least Squares Approach. Cambridge University Press, New York. [42] Jan, S., Samuel, P. and Lukas, F. (2007) Model Predictive Control of Building Heating System. Energy and Buildings, 43, 564-572. [43] Bohlin, P.T. (2006) Practical Grey-Box Process Identification. Springer, London. [44] Peterkas, V. (1981) Bayesian System Identification. Automatica, 17, 41-53. [45] Prívara, S., Siroky, J., Ferkl, L. and Cigler, J. (2011) Model Predictive Control of a Building Heating System: The First Experience. Energy and Buildings, 43, 564-572.
http://dx.doi.org/10.1016/j.enbuild.2010.10.022 [46] Zhao, H. and Magoulès, F. (2012) A Review on the Prediction of Building Energy Consumption. Renewable and Sustainable Energy Reviews, 16, 3586-3592.
http://dx.doi.org/10.1016/j.rser.2012.02.049 [47] McKinley, T.L. and Alleyne, A.G. (2008) Identification of Building Model Parameters and Loads Using On-Site Data Logs. Proceedings of the 3rd National Conference of IBPSA, Berkeley, 30 July-1 August 2008, 9-16. [48] Bargiotas, D., Birdwell, J.D. and Ieee, S. (1988) Residential Air Conditioner Dynamic Model for Direct Load Control. IEEE Transactions on Power Delivery, 3, 2119-2126.
http://dx.doi.org/10.1109/61.194024 [49] Chaitanya, K. (2009) Types of Heat Transfer.
http://www.castilloconfort.net/bio_estufas.html [50] Dewson, T., Day, B. and Irving, A.D. (1993) Least Squares Parameter Estimation of a Reduced Order Thermal Model of an Experimental Building. Building and Environment, 28, 127-137.
http://dx.doi.org/10.1016/0360-1323(93)90046-6 [51] Taylor, Z.T., Gowri, K. and Katipamula, S. (2008) GridLAB-D Technical Support Document: Residential End-Use Module Version 1.0. PNNL-17694, Pacific Northwest National Laboratory, Richland.
http://dx.doi.org/10.2172/939875 [52] Schneider, K.P., Member, S., Fuller, J.C. and Chassin, D.P. (2011) Multi-State Load Models for Distribution System Analysis. IEEE Transactions on Power Systems, 26, 2425-2433.
http://dx.doi.org/10.1109/TPWRS.2011.2132154 [53] Kalsi, K., Fuller, J., Elizondo, M. and Chassin, D. (2012) Aggregate Model for Heterogeneous Thermostatically Controlled Loads with Demand Response. Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, 22-26 July 2012, 1-8. [54] Behl, M., Nghiem, T.X. and Mangharam, R. (2014) IMpACT: Inverse Model Accuracy and Control Performance Toolbox for Buildings. Proceedings of the 2014 IEEE International Conference on Automation Science and Engineering (CASE), Taipei, 18-22 August 2014, 1-10. [55] Goyal, S. and Barooah, P. (2011) A Method for Model-Reduction of Nonlinear Building Thermal Dynamics. Proceedings of the 2011 American Control Conference (ACC), San Francisco, 29 June-1 July 2011, 2077-2082.
http://dx.doi.org/10.1109/ACC.2011.5991461 [56] Gyalistras, D. and Division, B.T. (2010) Use of Weather and Occupancy Forecasts for Optimal Building Climate Control (OptiControl): Two Years Progress Report. ETH Zurich, Technical Report. [57] Toffoli, E., Baldan, G., Albertin, G., Schenato, L., Chiuso, A. and Beghi, A. (2008) Thermodynamic Identification of Buildings Using Wireless Sensor Networks. Proceedings of the 17th IFAC World Congress, Seoul, 6-11 July 2008, 8860-8865. [58] Oldewurtel, F., Sagerschnig, C. and Eva, Z. (2013) Building Modeling as a Crucial Part for Building Predictive Control. Energy and Buildings, 56, 8-22.
http://dx.doi.org/10.1016/j.enbuild.2012.10.024 [59] Conseil National de Recherches Canada (2011) Codesnationaux.
http://www.nrc-cnrc.gc.ca/fra/publications/centre_codes/codes_guides.html [60] Foucquier, A., Robert, S., Suard, F., Stéphan, L. and Jay, A. (2013) State of the Art in Building Modelling and Energy Performances Prediction: A Review. Renewable and Sustainable Energy Reviews, 23, 272-288.
http://dx.doi.org/10.1016/j.rser.2013.03.004 [61] Millette, J., Sansregret, S. and Daoud, A. (2011) SIMEB: Simplified Interface to DOE2 and Energy Plus—A User’s Perspective—Case Study of an Existing Building. Laboratory of Energy Technologies, Hydro-Québec Research Institute. Proceedings of the 12th Conference of the International Building Performance Simulation Association, Sydney, 14-16 November 2011, 2349-2355. [62] Riedmiller, M. and Braun, H. (1993) A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. Proceedings of the IEEE International Conference on Neural Networks, San Francisco, 28 March-1 April 1993, 586-591.
http://dx.doi.org/10.1109/ICNN.1993.298623 [63] Riedmiller, M. (1994) Rprop—Description and Implementation Details. Technical Report, University of Karlsruhe, Karlsruhe.
[1] Halvgaard, R., Poulsen, N.K., Madsen, H. and JØrgensen, J.B. (2012) Economic Model Predictive Control for Building Climate Control in a Smart Grid. 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington DC, 16-20 January 2012, 1-6.
http://dx.doi.org/10.1109/ISGT.2012.6175631 [2] Haghighi, M.M. (2013) Controlling Energy-Efficient Buildings in the Context of Smart Grid: A Cyber Physical System Approach. Ph.D Dissertation, University of California at Berkeley, Berkeley. [3] Borlase, S., Ed. (2012) Smart Grids: Infrastructure, Technology, and Solutions. CRC Press, Boca Raton. [4] Zhou, Q., Wang, S.W., Xu, X.H. and Xiao, F. (2008) A Grey-Box Model of Next-Day Building Thermal Load Prediction for Energy-Efficient Control. International Journal of Energy Research, 32, 1418-1431.
http://dx.doi.org/10.1002/er.1458 [5] Crabb, J.A, Murdoch, N. and Penman, J.M. (1987) A Simplified Thermal Response Model. Building Service Engineering Research and Technology, 8, 13-19.
http://dx.doi.org/10.1177/014362448700800104 [6] Katipamula, S. and Brambley, M. (2005) Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part II. HVAC&R Research, 11, 169-187.
http://dx.doi.org/10.1080/10789669.2005.10391133 [7] Yang, Z.Y., Li, X.L., Tang, K., Yao, X., Bowers, C.P. and Schnier, T. (2012) An Efficient Evolutionary Approach to Parameter Identification in a Building Thermal Model. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42, 957-969. [8] Energy Solution Centre (2011) Easy$ Tip Sheets—Energy Advice Saving Yukoners Money. Energy Solution Centre Report, Whitehorse, 1-4. www.esc.gov.yk.ca [9] Pouresmaeil, E., Gonzalez, J.M., Bhattacharya, K. and Canizares, C.A. (2013) Development of a Smart Residential Load Simulator for Energy Management in Smart Grids. IEEE Transactions on Power Systems, 1-8. [10] Park, H., Ruellan, M., Bouvet, A., Monmasson, E. and Bennacer, R. (2011) Thermal parameter identification of simplified building model with electric appliance. 11th International Conference on Electrical Power Quality and Utilisation (EPQU), Lisbon, 17-19 October 2011, 1-6.
http://dx.doi.org/10.1109/EPQU.2011.6128822 [11] Jiménez, M.J. and Heras, M.R. (2009) Application of Different Dynamic Analysis Approaches to Estimate the U and G Values of Building Components. Building and Environment, 44, 361-367. [12] Merabtine, A. (2012) Modélisation Bond Graphs en vue de l’Efficacité énergétique du Batiment. Thesis, Université de Lorraine, Lorraine. [13] Zayane, C. (2011) Identification d’un modèle de comportement thermique de batiment à partir de sa courbe de charge. ParisTech, Paris. [14] Brause, R. (2010) Adaptive Modellierung und Simulation. Rüdiger Brause, Ed., Frankfurt. [15] Khan, M.E. and Farmeena, K. (2012) A Comparative Study of White Box, Black Box and Grey Box Testing Techniques. International Journal of Advanced Computer Science and Applications, 3, 12-15. [16] Berthou, T., Stabat, P., Salvazet, R. and Marchio, D. (2012) Comparaison de modèles linéaires inverses pour la mise en place de stratégies d’ effacement. Rencontres AUGC-IBPSA, 1-12. [17] Kawashima, M., Dorgan, C.E. and Mitchell, J.W. (1995) Hourly Thermal Load Prediction for the Next 24 Hours by ARIMA, EWMA, LR, and an Artificial Neural Network. ASHRAE Transactions, 101, 186. [18] Stevenson, W.J. (1994) Predicting Building Energy Parameters Using Artificial Neural Nets. Transactions of the American Society of Heating, Refrigerating and Airconditioning Engineers, 100, 1081-1087. [19] Ohlsson, M., Petersson, C., Pi, H., RÖgnvaldsson, T. and SÖderberg, B. (1994) Predicting System Loads with Artificial Neural Networks—Methods and Results from “The Great Energy Predictor Shootout”. ASHRAE Transactions, 100, 1063-1074. [20] Feuston, B.P. and Thurtell, J.H. (1994) Generalized Nonlinear Regression with Ensemble of Neural Nets: The Great Energy Predictor Shootout. ASHRAE Transactions, 100, 1075-1080. [21] Iijima, M., Takagi, K., Takeuchi, R. and Matsumoto, T. (1994) A Piecewise-Linear Regression on the ASHRAE Time-Series Data. ASHRAE Transactions, 100, 1088-1095. [22] Chen, C., Wang, J., Member, S., Heo, Y. and Kishore, S. (2013) MPC-Based Appliance Scheduling for Residential Building Energy Management Controller. IEEE Transactions on Smart Grid, 4, 1401-1410. [23] Szikra, C. (2014) Calculation of Heat Loss for Residential Buildings. [24] Singh, R. and Vyakaranam, B. (2012) Evaluation of Representative Smart Grid Investment Grant Project Technologies: Distributed Generation. PNNL, Richland.
http://www.esc.gov.yk.ca/ [25] Maasoumy, M., Moridian, B., Meysam, R. and Mahdi, S. (2013) Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control. Proceedings of the ASME Dynamic Systems and Control Conference, Palo Alto, 21-23 October 2013, 1-10. [26] Horváth, G. (2002) Neural Networks in System Modeling. In: Ablameyko, S., Goras, L., Gori, M. and Piuri, V., Eds., Neural Networks in Measurement Systems, IOS Press, Amsterdam, 43-78. [27] Verhelst, C. (2012) Model Predictive Control of Ground Coupled Heat Pump Systems for Office Buildings. Katholieke University Leuven, Leuven. [28] Christian, N., Dirk, J., Burhenne, S. and Florita, A. (2011) Modellbasierte Methoden für die Fehlererkennung und Optimierung im Gebäudebetrieb. Fraunhofer ISE, Technical Report 0327410A-C, 1-276. [29] Lebrun, J. (2001) Simulation of a HVAC System with the Help of an Engineering Equation Solver Plant of an Engineering Equation Solver. Proceedings of the 7th International IBPSA Conference, Rio de Janeiro, 13-15 August 2001, 1119-1126. [30] Deque, F., Ollivier, F. and Poblador, A. (2000) Grey Boxes Used to Represent Buildings with a Minimum Number of Geometric and Thermal Parameters. Energy and Buildings, 31, 29-35. [31] Verhelst, C., Logist, F., Van Impe, J. and Helsen, L. (2012) Study of the Optimal Control Problem Formulation for Modulating Air-to-Water Heat Pumps Connected to a Residential Floor Heating System. Energy and Buildings, 45, 43-53.
http://dx.doi.org/10.1016/j.enbuild.2011.10.015 [32] Ljung, L. (2009) System Identification: Theory for the User. Prentice-Hall, Upper Saddle River. [33] Tulleken, H.J.A.F. (1991) Application of the Grey-Box Approach to Parameter Estimation in Physicochemical Models. Proceedings of the 30th IEEE Conference on Decision and Control, Brighton, 11-13 December 1991, 1177-1183. [34] Clarke, J.A., Cockroft, J., Conner, S., Hand, J.W., Kelly, N.J., Moore, R., O’Brien, T. and Strachan, P. (2002) Simulation-Assisted Control in Building Energy Management Systems. Energy and Buildings, 34, 933-940.
http://dx.doi.org/10.1016/S0378-7788(02)00068-3 [35] Costanzo, G.T., Sossan, F., Marinelli, M., Bacher, P. and Madsen, H. (2013) Grey-Box Modeling for System Identification of Household Refrigerators: A Step toward Smart Appliances. Proceedings of the 4th International Youth Conference on Energy (IYCE), Siofok, 6-8 June 2013, 1-5. [36] Yudong, M. (2012) Model Predictive Control for Energy Efficient Buildings. University of California, Berkeley. [37] Deng, K. and Goyal, S. (2014) Structure-Preserving Model Reduction of Nonlinear Building Thermal Models. Automatica, 50, 1188-1195.
http://dx.doi.org/10.1016/j.automatica.2014.02.009 [38] Shariatzadeh, F. and Srivastava, A.K. (2013) Look-Ahead Control Approach for Thermostatic Electric Load in Distribution System. Proceedings of the 2013 North American Power Symposium (NAPS), Manhattan, 22-24 September 2013, 1-6.
http://dx.doi.org/10.1109/NAPS.2013.6666878 [39] Braun, J.E. (1990) Reducing Energy Costs and Peak Electrical Demand through Optimal Control of Building Thermal Storage. ASHRAE Transactions, 96, 876-888. [40] Siroký, J., Oldewurtel, F., Cigler, J. and Prívara, S. (2011) Experimental Analysis of Model Predictive Control for an Energy Efficient Building Heating System. Applied Energy, 88, 3079-3087.
http://dx.doi.org/10.1016/j.apenergy.2011.03.009 [41] Verhaegen, M. and Verdult, V. (2012) Filtering and System Identification: A Least Squares Approach. Cambridge University Press, New York. [42] Jan, S., Samuel, P. and Lukas, F. (2007) Model Predictive Control of Building Heating System. Energy and Buildings, 43, 564-572. [43] Bohlin, P.T. (2006) Practical Grey-Box Process Identification. Springer, London. [44] Peterkas, V. (1981) Bayesian System Identification. Automatica, 17, 41-53. [45] Prívara, S., Siroky, J., Ferkl, L. and Cigler, J. (2011) Model Predictive Control of a Building Heating System: The First Experience. Energy and Buildings, 43, 564-572.
http://dx.doi.org/10.1016/j.enbuild.2010.10.022 [46] Zhao, H. and Magoulès, F. (2012) A Review on the Prediction of Building Energy Consumption. Renewable and Sustainable Energy Reviews, 16, 3586-3592.
http://dx.doi.org/10.1016/j.rser.2012.02.049 [47] McKinley, T.L. and Alleyne, A.G. (2008) Identification of Building Model Parameters and Loads Using On-Site Data Logs. Proceedings of the 3rd National Conference of IBPSA, Berkeley, 30 July-1 August 2008, 9-16. [48] Bargiotas, D., Birdwell, J.D. and Ieee, S. (1988) Residential Air Conditioner Dynamic Model for Direct Load Control. IEEE Transactions on Power Delivery, 3, 2119-2126.
http://dx.doi.org/10.1109/61.194024 [49] Chaitanya, K. (2009) Types of Heat Transfer.
http://www.castilloconfort.net/bio_estufas.html [50] Dewson, T., Day, B. and Irving, A.D. (1993) Least Squares Parameter Estimation of a Reduced Order Thermal Model of an Experimental Building. Building and Environment, 28, 127-137.
http://dx.doi.org/10.1016/0360-1323(93)90046-6 [51] Taylor, Z.T., Gowri, K. and Katipamula, S. (2008) GridLAB-D Technical Support Document: Residential End-Use Module Version 1.0. PNNL-17694, Pacific Northwest National Laboratory, Richland.
http://dx.doi.org/10.2172/939875 [52] Schneider, K.P., Member, S., Fuller, J.C. and Chassin, D.P. (2011) Multi-State Load Models for Distribution System Analysis. IEEE Transactions on Power Systems, 26, 2425-2433.
http://dx.doi.org/10.1109/TPWRS.2011.2132154 [53] Kalsi, K., Fuller, J., Elizondo, M. and Chassin, D. (2012) Aggregate Model for Heterogeneous Thermostatically Controlled Loads with Demand Response. Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, 22-26 July 2012, 1-8. [54] Behl, M., Nghiem, T.X. and Mangharam, R. (2014) IMpACT: Inverse Model Accuracy and Control Performance Toolbox for Buildings. Proceedings of the 2014 IEEE International Conference on Automation Science and Engineering (CASE), Taipei, 18-22 August 2014, 1-10. [55] Goyal, S. and Barooah, P. (2011) A Method for Model-Reduction of Nonlinear Building Thermal Dynamics. Proceedings of the 2011 American Control Conference (ACC), San Francisco, 29 June-1 July 2011, 2077-2082.
http://dx.doi.org/10.1109/ACC.2011.5991461 [56] Gyalistras, D. and Division, B.T. (2010) Use of Weather and Occupancy Forecasts for Optimal Building Climate Control (OptiControl): Two Years Progress Report. ETH Zurich, Technical Report. [57] Toffoli, E., Baldan, G., Albertin, G., Schenato, L., Chiuso, A. and Beghi, A. (2008) Thermodynamic Identification of Buildings Using Wireless Sensor Networks. Proceedings of the 17th IFAC World Congress, Seoul, 6-11 July 2008, 8860-8865. [58] Oldewurtel, F., Sagerschnig, C. and Eva, Z. (2013) Building Modeling as a Crucial Part for Building Predictive Control. Energy and Buildings, 56, 8-22.
http://dx.doi.org/10.1016/j.enbuild.2012.10.024 [59] Conseil National de Recherches Canada (2011) Codesnationaux.
http://www.nrc-cnrc.gc.ca/fra/publications/centre_codes/codes_guides.html [60] Foucquier, A., Robert, S., Suard, F., Stéphan, L. and Jay, A. (2013) State of the Art in Building Modelling and Energy Performances Prediction: A Review. Renewable and Sustainable Energy Reviews, 23, 272-288.
http://dx.doi.org/10.1016/j.rser.2013.03.004 [61] Millette, J., Sansregret, S. and Daoud, A. (2011) SIMEB: Simplified Interface to DOE2 and Energy Plus—A User’s Perspective—Case Study of an Existing Building. Laboratory of Energy Technologies, Hydro-Québec Research Institute. Proceedings of the 12th Conference of the International Building Performance Simulation Association, Sydney, 14-16 November 2011, 2349-2355. [62] Riedmiller, M. and Braun, H. (1993) A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. Proceedings of the IEEE International Conference on Neural Networks, San Francisco, 28 March-1 April 1993, 586-591.
http://dx.doi.org/10.1109/ICNN.1993.298623 [63] Riedmiller, M. (1994) Rprop—Description and Implementation Details. Technical Report, University of Karlsruhe, Karlsruhe.