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.