J. Afram, . Afram, ;. Abdul, ;. Farrokh, . Ajib et al., Theory and applications of HVAC control systems -A review of model predictive control (MPC), In: Building and Environment, vol.72, 2014.

S. Lecoeuche, Building thermal modeling using a hybrid system approach, Preprints of the 20th World Congress The International Federation of Automatic Control IFAC, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01720374

. Ajib, , 2018.

S. Lecoeuche, Predicting the air temperature of a building zone by detecting different configurations using a switched system identification technique, 2018.

. Ajib, , 2018.

. Lecoeuche, ;. Stéphane, and J. Gauvrit, Prediction of standardized energy consumption of existing buildings based on hybrid systems modeling and control, 57th IEEE Conference on Decision and Control (CDC), 2018.
URL : https://hal.archives-ouvertes.fr/hal-02285180

. Ajib, Building thermal modeling using switching systems, International Building Performance Simulation Association IBPSA, 2016.

. Al-saadi, N. ;. Saleh, and . Zhai, Zhiqiang: A new validated TRNSYS module for simulating latent heat storage walls, In: Energy and Buildings, vol.109, 2015.

F. Allard and C. Inard, Natural and mixed convection in rooms: prediction of thermal stratification and heat transfer by zonal models, Proceedings of ISRACVE, pp.335-342, 1992.

E. Amasyali, . Amasyali, ;. Kadir, and N. M. El-gohary, A review of data-driven building energy consumption prediction studies, Renewable and Sustainable Energy Reviews, vol.81, 2018.

[. Amayri, , 2016.

V. R. Badarla, Estimating occupancy in heterogeneous sensor environment, In: Energy and Buildings, vol.129, pp.46-58, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01864741

. Bibliography-[amiri, Using multiple regression analysis to develop energy consumption indicators for commercial buildings in the U, S. In: Energy and Buildings, vol.109, pp.209-216, 2015.

. Andelkovic, Experimental validation of a EnergyPlus model: Application of a multistorey naturally ventilated double skin façade, In: Energy and Buildings, vol.118, pp.27-36, 2016.

. Andersen, Modelling the heat dynamics of a building using stochastic differential equations, In: Energy and Buildings, vol.31, 2000.

. Arregi, ;. Beñat, and R. Garay, Regression analysis of the energy consumption of tertiary buildings, Energy Procedia, vol.122, pp.9-14, 2017.

K. J. Åström and T. Hägglund, PID controllers: theory, design, and tuning. Bd. 2. Instrument society of, 1995.

. Bacher, ;. Peder, and H. Madsen, Identifying suitable models for the heat dynamics of buildings, In: Energy and Buildings, vol.43, issue.7, pp.1511-1522, 2011.

L. Bako, Contribution à l'identification de systèmes dynamiques hybrides, 2008.

M. Bauer, J. ;. Scartezzini, A. ;. Bemporad, and G. Ferrari-trecate, A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings, In: Energy and Buildings, vol.27, pp.147-154, 1998.

M. Morari, Observability and controllability of piecewise affine and hybrid systems, IEEE transactions on automatic control, vol.45, issue.10, pp.1864-1876, 2000.

A. ;. Bemporad and M. Morari, Control of systems integrating logic, dynamics, and constraints, vol.35, pp.407-427, 1999.

A. ;. Bemporad and M. Morari, Control of systems integrating logic, dynamics, and constraints, vol.35, pp.407-427, 1999.

H. Bouia and P. Dalicieux, Simplified modeling of air movements inside dwelling room, Proceedings of Building Simulation'91 Conference, pp.106-110, 1991.

[. Boukharouba, , 2010.

S. Lecoeuche, Multimodeling vs piecewise affine modeling for the identification of open channel systems, Large Scale Complex Systems Theory and Applications Bd. 9, pp.474-479, 2010.

, Khaled: Modélisation et classification de comportements dynamiques des systemes hybrides, 2011.

[. Boukharouba, Identification of piecewise affine systems based on dempster-shafer theory, System Identification Bd, vol.15, pp.1662-1667, 2009.

[. Boyer, Thermal building simulation and computer generation of nodal models, Building and Environment, vol.31, issue.3, pp.207-214, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00766238

[. Bozonnet, , 2005.

F. Allard, Modelling solar effects on the heat and mass transfer in a street canyon, a simplified approach, In: Solar Energy, vol.79, issue.1, pp.10-24, 2005.

. Brastein, Building performance simulation using Modelica: analysis of the current state and application areas, 13th International Conference of the International Building Performance Simulation Association, vol.169, pp.58-68, 2013.

[. Canty, O. Canty, ;. Niel, and T. Mahony, Design considerations for piecewise affine system identification of nonlinear systems, Control and Automation, 2009. MED'09. 17th Mediterranean Conference on IEEE, pp.157-162, 2009.

[. Canyurt, , 2005.

Z. Utlu, Estimating the Turkish residential-commercial energy output based on genetic algorithm (GA) approaches, In: Energy Policy, vol.33, pp.1011-1019, 2005.

[. Caucheteux, V: Occupancy measurement in building: A litterature review, application on an energy efficiency research demonstrated building, International Journal of Metrology and Quality Engineering, vol.4, issue.2, pp.135-144, 2013.

. Bibliography-[caucheteux, Mesure de l'occupation pour l'évaluation de la performance énergétique des bâtiments : plan de mesures et incertitudes, International Building Performance Simulation Association IBPSA, 2016.

C. Chang, , 2011.

C. Lin, LIBSVM: a library for support vector machines, ACM transactions on intelligent systems and technology (TIST), vol.2, p.27, 2011.

[. Clarke, ;. Mclean, J. ;. Clarke, and D. Mclean, ESP-A building and plant energy simulation system, Strathclyde: Energy Simulation Research Unit, 1988.

C. ;. Cortes and V. Vapnik, Support-vector networks, Machine learning, vol.20, pp.273-297, 1995.

[. Costa, Discrete-time Markov jump linear systems, 2006.

[. Crabb, A simplified thermal response model, Building Services Engineering Research and Technology, vol.8, issue.1, pp.13-19, 1987.

[. Crawley, En-ergyPlus: creating a new-generation building energy simulation program, Energy and buildings, vol.33, pp.319-331, 2001.

K. W. Dahanayake, C. ;. Kalani, . Chow, and L. Cheuk, Studying the potential of energy saving through vertical greenery systems: Using EnergyPlus simulation program, In: Energy and Buildings, vol.138, 2017.

B. De-schutter, . Boom, and . Van, Model predictive control for max-min-plus-scaling systems, Proceedings of the 2001 Bd. 1 IEEE, pp.319-324, 2001.

D. Schutter, ;. De-schutter, and B. , Optimal control of a class of linear hybrid systems with saturation, SIAM Journal on Control and Optimization, vol.39, issue.3, pp.835-851, 2000.

[. Deb, Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings, Solar Energy, vol.163, 2018.

C. Do-valle, Continuous-time Markov jump linear systems, 2012.

. Dong, Applying support vector machines to predict building energy consumption in tropical region, In: Energy and Buildings, vol.37, 2005.

. Doucet, Particle filters for state estimation of jump Markov linear systems, IEEE Transactions on signal processing, vol.49, issue.3, pp.613-624, 2001.

[. Europa-]-europa, COMMISSION: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. Energy Roadmap 2050, Official Journal of the European Union, vol.315, pp.96-107, 2004.

[. Fazenda, Contextbased thermodynamic modeling of buildings spaces, In: Energy and Buildings, vol.124, pp.164-177, 2016.

. Fels, PRISM: an introduction. In: Energy and Buildings, vol.9, pp.5-18, 1986.

[. Ferracuti, Data-driven models for short-term thermal behaviour prediction in real buildings, Adaptation in natural and artificial systems, vol.204, pp.1375-1387, 1975.

. Bibliography-[foucquier, , 2013.

L. ;. Stéphan and A. Jay, State of the art in building modelling and energy performances prediction: A review, Renewable and Sustainable Energy Reviews, vol.23, pp.272-288, 2013.

[. Fraisse, , 2002.

G. Achard, Development of a simplified and accurate building model based on electrical analogy, In: Energy and Buildings, vol.34, pp.1017-1031, 2002.

[. Freire, Development of regression equations for predicting energy and hygrothermal performance of buildings, Nr. 5, S. 810 -820, vol.40, 2008.

[. Fu, Using Support Vector Machine to Predict Next Day Electricity Load of Public Buildings with Sub-metering Devices, The 9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) joint with the 3rd International Conference on Building Energy and Environment (COBEE), vol.121, pp.12-15, 2015.

C. F. Gao and W. L. Lee, Evaluating the influence of openings configuration on natural ventilation performance of residential units in Hong Kong, In: Building and Environment, vol.46, 2011.

[. Geng, Building energy performance diagnosis using energy bills and weather data, In: Energy and Buildings, 2018.

]. Goldberg and . De, Genetic algorithms in search, optimization, and machine learning, addison-wesley, reading, ma, Google Scholar, 1989.

. Goyal, ;. Siddharth, and P. Barooah, A method for model-reduction of non-linear thermal dynamics of multi-zone buildings, Energy and Buildings, vol.47, 2012.

G. ;. Guerassimoff and J. Thomas, Enhancing energy efficiency and technical and marketing tools to change people's habits in the long-term, In: Energy and Buildings, vol.104, 2015.

[. Guo, Study on Natural Ventilation Design Optimization Based on CFD Simulation for Green Buildings, Procedia Engineering, vol.121, pp.573-581, 2015.

, The 9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) joint with the 3rd International Conference on Building Energy and Environment (COBEE), pp.12-15, 2015.

. Haghighat, Development and validation of a zonal model-POMA, Building and environment, vol.36, issue.9, pp.1039-1047, 2001.

[. Harb, Development and validation of grey-box models for forecasting the thermal response of occupied buildings, In: Energy and Buildings, vol.117, pp.199-207, 2016.

V. S. Harish and A. Kumar, A review on modeling and simulation of building energy systems, In: Renewable and Sustainable Energy Reviews, vol.56, 2016.

. Hasan, A simplified building thermal model for the optimization of energy consumption: Use of a random number generator, In: Energy and Buildings, vol.82, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01657162

[. Hilliaho, Glazed space thermal simulation with IDA-ICE 4.61 software-Suitability analysis with case study, Energy and Buildings, vol.89, 2015.

. Hong, A decision support model for reducing electric energy consumption in elementary school facilities, In: Energy Conversion and Management, vol.95, pp.2479-2490, 2006.

. Huang, An Integrated Zonal Model for Predicting Indoor Airflow, Temperature, and VOC Distributions, vol.111, 2005.

, IDA: indoor climate and energy 4, IDA-ICE, vol.1, 2014.

K. ;. Kashima, . Kusano, ;. Masami, . Ikeda, ;. Tsukasa et al., Identification of the main thermal characteristics of building components using MAT-LAB, Analysis and Modelling of Building Components, vol.368, pp.170-180, 2008.

[. Jiménez, Identification of the main thermal characteristics of building components using MATLAB, Building and Environment, vol.43, 2008.

[. Joseph, Piecewise Affine Modeling of a Hybrid 3-Tank System, 2015.

[. Joseph, Generation of Piecewise-Affine Model from A Mixed-Logic Dynamical Model of a 3-Tank System, Journal of Advanced Research in Dynamical and Control Systems, vol.9, issue.2, pp.19-23, 2017.

S. A. Kalogirou, Applications of artificial neuralnetworks for energy systems, In: Applied Energy, vol.67, 2000.

S. A. Kalogirou, Artificial neural networks in energy applications in buildings, International Journal of Low-Carbon Technologies, vol.1, issue.3, pp.201-216, 2006.

S. A. Kalogirou and M. Bojic, Artificial neural networks for the prediction of the energy consumption of a passive solar building, Nr. 5, S. 479 -491, vol.25, 2000.

L. ;. Libessart, . Lassue, ;. Stéphane, and D. Defer, Impact of plants occultation on energy balance: Experimental study, In: Energy and Buildings, vol.162, pp.208-218, 2018.

[. Kenjo, Experimental and numerical study of thermal stratification in a mantle tank of a solar domestic hot water system, In: Applied Thermal Engineering, vol.27, issue.11, 1986.
URL : https://hal.archives-ouvertes.fr/hal-00312242

[. Khayatian, Application of neural networks for evaluating energy performance certificates of residential buildings, In: Energy and Buildings, vol.125, 2016.

]. Kolokotsa and . Dionysia, The role of smart grids in the building sector, Energy and Buildings, vol.116, 2016.

[. Koulamasa, Suitability analysis of modeling and assessment approaches in energy efficiency in buildings, In: Energy and Buildings, vol.158, 2018.

[. Kumar, Energy analysis of a building using artificial neural network: A review, In: Energy and Buildings, vol.65, 2013.

[. Kuznik, , 2011.

M. ;. Woloszyn and J. Roux, Numerical modelling of combined heat transfers in a double skin façade -Full-scale laboratory experiment validation, In: Applied Thermal Engineering, vol.31, 2011.

[. Lai, Vapnik's learning theory applied to energy consumption forecasts in residential buildings, International Journal of Computer Mathematics, vol.85, pp.1563-1588, 2008.

, Louis: Contribution au développement de modèles mathématiques du comportement thermique transitoire de structures d'habitation. Universite de liege faculte des sciences appliquees laboratoire de physique du batiment, 1980.

]. Lebrun and J. , Exigences physiologiques et modalités physiques de la climatisation par source statique concentrée, Rédaction et administration: Prof. L. Leloup (Institut de mécanique), 1971.

D. Lee and . Sang-hoon, Impacts of surrounding building layers in CFD wind simulations, Energy Procedia, vol.122, 2017.

[. Lee, , 2004.

J. M. House, N. -. Kyong, and . Ho, Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks, In: Applied Energy, vol.77, 2004.

[. Lee, , 1996.

. Park, ;. Cheol, and G. E. Kelly, Fault detection in an air-handling unit using residual and recursive parameter identification methods, Transactions-American Society Of Heating Refrigerating And Air Conditioning Engineers, vol.102, pp.528-539, 1996.

J. Y. Lettvin, H. R. Maturana, W. S. Mccul-loch, . Pitts, and H. Walter, What the frog's eye tells the frog's brain, Proceedings of the IRE, vol.47, pp.1940-1951, 1959.

[. Li, , 2009.

A. Mochida, Applying support vector machine to predict hourly cooling load in the building, In: Applied Energy, vol.86, 2009.

[. Li, , 2009.

A. Mochida, Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks, In: Energy Conversion and Management, vol.50, 2009.

. Lin, Issues in identification of control-oriented thermal models of zones in multi-zone buildings, Decision and Control (CDC), 2012 IEEE 51st Annual Conference on IEEE, pp.6932-6937, 2012.

, LJUNG, Lennart: System identification, 1998.

[. Luo, ;. Ariyur, . Luo, ;. Qi, . Ariyur et al., Building thermal network model and application to temperature regulation, 2010 IEEE International Conference on Control Applications, pp.2190-2195, 2010.

[. Lü, Modeling and forecasting energy consumption for heterogeneous buildings using a physical-statistical approach, In: Applied Energy, vol.144, 2015.

[. Macarulla, , 2017.

. Forcada, ;. Núria, and M. Gangolells, Modelling indoor air carbon dioxide concentration using grey-box models, In: Building and Environment, vol.117, pp.146-153, 2017.

H. Madsen and J. Holst, Estimation of continuous-time models for the heat dynamics of a building, In: Energy and Buildings, vol.22, 1995.

. Magalhães, Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior, vol.151, pp.332-343, 2017.

, MATLAB: The mathworks MATLAB User's guide, Inc, vol.5, 1998.

, Yoshimasa: Bilinear transformation method, 1984.

[. Mayer, Management of hybrid energy supply systems in buildings using mixed-integer model predictive control, In: Energy Conversion and Management, vol.98, 2015.

A. Mechaqrane and M. Zouak, A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building, Neural Computing & Applications, vol.13, issue.1, pp.32-37, 2004.

A. C. Megri and F. Haghighat, Zonal modeling for simulating indoor environment of buildings: Review, recent developments, and applications, Hvac&R Research, vol.13, issue.6, pp.887-905, 2007.

A. C. Megri and Y. Yu, New calibrated zonal model (POMA+) for temperature and airflow predictions, In: Building and Environment, vol.94, 2015.

]. Mora and L. , Prédiction des performances thermo-aérauliques des bâtiments par association de modèles de différents niveaux de finesse au sein d'un environnement orienté objet, 2003.

[. Mottahedi, , 2015.

S. S. Amiri, . Riley, ;. David, and S. Asadi, Multi-linear Regression Models to Predict the Annual Energy Consumption of an Office Building with Different Shapes, S1877705815021505. -Defining the future of sustainability and resilience in design, engineering and construction, vol.118, pp.622-629, 2015.

[. Mustafaraj, Development of room temperature and relative humidity linear parametric models for an open office using BMS data, In: Energy and Buildings, vol.42, issue.3, pp.348-356, 2010.

[. Musy, , 2002.

A. Sergent, Automatically generated zonal models for building air flow simulation: principles and applications, Building and Environment, vol.37, pp.873-881, 2002.

[. Nageler, Validation of dynamic building energy simulation tools based on a real test-box with thermally activated building systems (TABS), In: Energy and Buildings, vol.168, pp.42-55, 2018.

. Naveros, Physical parameters identification of walls using ARX models obtained by deduction, In: Energy and Buildings, vol.108, pp.317-329, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01236357

, NGENDAKUMANA, Philippe: Modélisation simplifiée du comportement thermique d'un bâtiment et vérification expérimentale, 1991.

P. V. Nielsen, . Ohlsson, ;. Henrik, and L. Ljung, Identification of Piecewise Affine Systems Using Sum-of-Norms Regularization. In: IFAC Proceedings Volumes, vol.44, 1976.

T. Olofsson and S. Andersson, Long-term energy demand predictions based on short-term measured data, In: Energy and Buildings, vol.33, issue.2, pp.85-91, 2001.

. Olofsson, Energy load predictions for buildings based on a total demand perspective, In: Energy and Buildings, vol.28, 1998.

. Ooka, ;. Ryozo, and K. Komamura, Optimal design method for building energy systems using genetic algorithms, Building and Environment, vol.44, issue.7, pp.1538-1544, 2009.

[. Ozturk, Electricity estimation using genetic algorithm approach: a case study of Turkey, In: Energy, vol.30, issue.7, pp.1003-1012, 2005.

. Pan, Study on simulation methods of atrium building cooling load in hot and humid regions, In: Energy and Buildings, vol.42, 2010.

[. Paoletti, Modeling of a building system and its parameter identification, European Journal of Control, vol.13, issue.2, pp.975-983, 2007.

M. ;. Parti and C. Parti, The total and appliancespecific conditional demand for electricity in the household sector, The Bell Journal of Economics, pp.309-321, 1980.

. Paulus, Algorithm for automating the selection of a temperature dependent change point model, In: Energy and Buildings, vol.87, pp.95-104, 2015.

B. ;. Peuportier and I. B. Sommereux, Simulation tool with its expert interface for the thermal design of multizone buildings, International Journal of Solar Energy, vol.8, issue.2, pp.109-120, 1990.
URL : https://hal.archives-ouvertes.fr/hal-00520077

D. Prakash and P. Ravikumar, Analysis of thermal comfort and indoor air flow characteristics for a residential building room under generalized window opening position at the adjacent walls, International Journal of Sustainable Built Environment, vol.4, issue.1, pp.42-57, 2015.

. Pérez-lombard, A review on buildings energy consumption information. In: Energy and Buildings, vol.40, 2008.

. Qin, Research on a dynamic simulation method of atrium thermal environment based on neural network, In: Building and Environment, vol.50, 2012.

, A: Parameter estimation in buildings: methods for dynamic analysis of measured energy use, Journal of Solar Energy Engineering, vol.110, issue.1, pp.52-66, 1988.

L. H. Rajaoarisoa, N. K. Sirdi, and J. F. Balmat, Micro-climate optimal control for an experimental greenhouse automation, Communications, Computing and Control Applications (CCCA), pp.1-6, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01778916

T. ;. Recht, . Munaretto, ;. Fabio, and P. Schalbart,

B. Peuportier, Analyse de la fiabilité de COMFIE par comparaison à des mesures. Application à un bâtiment passif, 2014.

. Reynders, Quality of greybox models and identified parameters as function of the accuracy of input and observation signals, In: Energy and Buildings, vol.82, 2014.

[. Rouchier, Identification of Envelope Hygrothermal Properties Based on In-situ Sensor Measurements and Stochastic Inverse Methods. In: Energy Procedia, vol.78, pp.943-948, 2015.

[. Royer, , 2014.

M. ;. Polit and . Ríos-moreno, Modelling temperature in intelligent buildings by means of autoregressive models, Black-box modeling of buildings thermal behavior using system identification, vol.47, pp.10850-10855, 2007.

]. Sandberg and M. , A model for ventilation by displacement, Room Vent-87, 1987.

Y. ;. Sang, J. R. Zhao, . Sun, ;. Jiaojiao, . Chen et al., Experimental investigation and EnergyPlus-based model prediction of thermal behavior of building containing phase change material, In: Journal of Building Engineering, vol.12, pp.259-266, 2017.

]. Scanu and L. , Towards archetypes of self-tuned models for connected buildings, 2017.
URL : https://hal.archives-ouvertes.fr/tel-01736999

. Schumacher, Linear complementarity systems, SIAM journal on applied mathematics, vol.60, pp.1234-1269, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00756195

[. Shabunko, EnergyPlus models for the benchmarking of residential buildings in Brunei Darussalam, In: Energy and Buildings, 2016.

. Shein, PID controller for temperature control with multiple actuators in cyber-physical home system, Network-based information systems (NBiS), 2012 15th international conference on IEEE, pp.423-428, 2012.

. Sodagar, ;. Behzad, and D. Starkey, The monitored performance of four social houses certified to the Code for Sustainable Homes Level 5, In: Energy and Buildings, vol.110, pp.245-256, 2016.

R. C. Sonderegger, Dynamic models of house heating based on equivalent thermal parameters, 1978.

. Song, ;. Jiafang, and X. Meng, The Improvement of Ventilation Design in School Buildings Using CFD Simulation, The 9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) joint with the 3rd International Conference on Building Energy and Environment (COBEE), vol.121, pp.12-15, 2015.

E. Sontag, Nonlinear regulation: The piecewise linear approach, IEEE Transactions on automatic control, vol.26, issue.2, pp.346-358, 1981.

A. M. Soto, . Jentsch, and F. Mark, Comparison of prediction models for determining energy demand in the residential sector of a country, In: Energy and Buildings, vol.128, 2016.

E. F. Sowell and P. Haves, Efficient solution strategies for building energy system simulation, Energy and buildings, vol.33, pp.309-317, 2001.

, LBNL: 2.0 reference manual, TH-BÂT: Règles TH-U pour les bâtiments existants, 2003.

. Tian, Building energy simulation coupled with CFD for indoor environment: A critical review and recent applications, In: Energy and Buildings, vol.165, pp.184-199, 2018.

. Tittelein, Simspark platform evolution for low-energy building simulation, pp.25-29, 2008.

, A: Transient System Simulation Program, 2000.

[. Turner, Residential HVAC fault detection using a system identification approach, Energy and Buildings, vol.151, pp.1-17, 2017.

]. Vapnik and V. , Pattern recognition using generalized portrait method, Automation and remote control, vol.24, 1963.

, Vladimir: The nature of statistical learning theory, 2013.

[. Va?ak, Identification of a discrete-time piecewise affine model of a pitch-controlled wind turbine, MIPRO, 2011 Proceedings of the 34th International Convention IEEE, pp.744-749, 2011.

. Vidal, Observability and identifiability of jump linear systems, Proceedings of the 41st IEEE Conference on Bd. 4 IEEE, pp.3614-3619, 2002.

. Vidal, ;. René, and Y. Ma, A unified algebraic approach to 2-D and 3-D motion segmentation and estimation, Journal of Mathematical Imaging and Vision, vol.25, issue.3, pp.403-421, 2006.

J. I. Videla, . Lie, and . Bernt, Library for modeling and simulating the thermal dynamics of buildings, 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering Bd. 21, pp.1777-1782, 2006.

É. Vorger, Étude de l'influence du comportement des habitants sur la performance énergétique du bâtiment, 2014.

. Vries, Hybrid system modeling and identification of cell biology systems: perspectives and challenges, IFAC Proceedings Volumes, vol.42, pp.227-232, 2009.

. Wang, ;. Haidong, and Z. Zhai, Advances in building simulation and computational techniques: A review between 1987 and, vol.128, pp.319-335, 2014.

. Wang, ;. Shengwei, and X. Xu, Simplified building model for transient thermal performance estimation using GA-based parameter identification, International Journal of Thermal Sciences, vol.45, 2006.

[. Wang, Quantitative energy performance assessment methods for existing buildings, Cool Pavements, Cool Cities, and Cool World, vol.55, 2012.

[. Wetter, Modelica Buildings library 2.0. In: Building Simulation, 2015.

[. Witrant, A hybrid model and MIMO control for intelligent buildings temperature regulation over WSN, 8th IFAC workshop on time delay systems, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00444556

]. Wurtz and E. , Three-dimensional modeling of thermal and airflow transfers in building using an object-oriented simulation environment, 1995.

[. Xiao, A fault detection and diagnosis strategy with enhanced sensitivity for centrifugal chillers, In: Applied Thermal Engineering, vol.31, 2011.

. Xin, Piecewise affine approximations for quality modeling and control of perishable foods, Optimal Control Applications and Methods, vol.39, issue.2, pp.860-872, 2018.

J. ;. Xu and L. Xie, Control and estimation of piecewise affine systems, 2014.

[. Xu, Hybrid model predictive control of active suspension with travel limits and nonlinear tire contact force, American Control Conference (ACC), pp.2415-2420, 2016.

D. ;. Yang and L. Jiang, Simulation study on the Natural Ventilation of College Student' Dormitory, Procedia Engineering 205, 2017.

, 10th International Symposium on Heating, Ventilation and Air Conditioning, pp.1279-1285, 2017.

. Yu, ;. Hwanjo, and S. Kim, SVM tutorial-classification, regression and ranking, pp.479-506, 2012.

[. Yu, Hiroshi: A decision tree method for building energy demand modeling, Energy and Buildings, vol.42, issue.10, pp.1637-1646, 2010.

[. Yun, Building hourly thermal load prediction using an indexed ARX model, In: Energy and Buildings, vol.54, pp.225-233, 2012.

[. Zhai, On approaches to couple energy simulation and computational fluid dynamics programs, Building and Environment, vol.37, 2002.

. Zhang, ;. Qinghua, and L. Ljung, Multiple steps prediction with nonlinear ARX models, 6th IFAC Symposium on Nonlinear Control Systems, vol.37, 2004.

. Zhang, Simulation analysis on summer conditions of ancient architecture of tower buildings based on CFD, Energy Procedia, vol.143, pp.313-319, 2017.

H. Zhao, ;. Magoulès, and F. , A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews, vol.16, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00802029

[. Zhao, Occupant-oriented mixed-mode EnergyPlus predictive control simulation, Energy and Buildings, vol.117, 2016.

[. Zhao, , 2014.

V. Loftness, Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining, In: Energy and Buildings, vol.82, pp.341-355, 2014.