A review on modeling and simulation of building energy systems

Buildings consume about 40% of the overall energy consumption, worldwide and correspondingly are also responsible for carbon emissions. Since, last decade efforts have been made to reduce this share of CO2 emissions by energy conservation and efficient measures. Scientist across the world is working on energy modeling and control in order to develop strategies that would result in an overall reduction of a building׳s energy consumption. Development of control strategies asks for a computationally efficient energy model of a building under study. This paper presents a review of all the significant modeling methodologies which have been developed and adopted to model the energy systems of buildings. Attention is majorly focused on the works which involved development of the control strategies by modeling the building energy systems. Models reviewed are presented categorically as per the modeling approach adopted by the researchers. Simulation programs and softwares available for building energy modeling are also presented.

[1]  Costas S. Mitropoulos,et al.  Energy-economy models: A survey , 1982 .

[2]  V. S. K. V. Harish,et al.  Techniques used to construct an energy model for attaining energy efficiency in building: A review , 2014, Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC).

[3]  M. Ramachandran,et al.  Application of multi-criteria decision making to sustainable energy planning--A review , 2004 .

[4]  Egidijus Kazanavičius,et al.  THE HEAT BALANCE MODEL OF RESIDENTIAL HOUSE , 2015 .

[5]  George Loisos,et al.  Quick Design Analysis for Improving Building Energy Performance , 2014 .

[6]  Shengwei Wang,et al.  Transient heat flow calculation for multilayer constructions using a frequency-domain regression method , 2003 .

[7]  Peter Townsend.  'Absolute' and 'overall' poverty: the Copenhagen approach to the fulfilment of human rights , 2006 .

[8]  Leslie K. Norford,et al.  Empirical Modeling of a Rolling- Piston Compressor Heat Pump for Predictive Control in Low Lift Cooling , 2010 .

[9]  Lino Guzzella,et al.  Optimal design and operation of building services using mixed-integer linear programming techniques , 2013 .

[10]  David E. Claridge,et al.  Multivariate Regression Modeling , 1998 .

[11]  A. Rabl,et al.  Energy signature models for commercial buildings: test with measured data and interpretation , 1992 .

[12]  D. G. Stephenson,et al.  Room thermal response factors , 1967 .

[13]  M. M. Gouda,et al.  Building thermal model reduction using nonlinear constrained optimization , 2002 .

[14]  Hakgeun Jeong,et al.  Analysis of the effect of artificial lighting on heating and cooling energy in commercial buildings , 2014 .

[15]  Arun Kumar,et al.  Reduced order modeling and parameter identification of a building energy system model through an optimization routine , 2016 .

[16]  Peter Nijkamp,et al.  New directions in integrated regional energy planning , 1990 .

[17]  David E. Claridge,et al.  Modeling Hourly Energy Use in Commercial Buildings With Fourier Series Functional Forms , 1998 .

[18]  Ibrahim Kavrakoǧlu,et al.  Multiobjective strategies in power systems planning , 1983 .

[19]  T. Agami Reddy,et al.  Literature review on calibration of building energy simulation programs : Uses, problems, procedures, uncertainty, and tools , 2006 .

[20]  David E. Claridge,et al.  A Change-Point Principal Component Analysis (CP/PCA) Method for Predicting Energy Usage in Commercial Buildings: The PCA Model , 1993 .

[21]  Shengwei Wang,et al.  Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm , 2007 .

[22]  Gilles Fraisse,et al.  Development of a simplified and accurate building model based on electrical analogy , 2002 .

[23]  David E. Claridge,et al.  Baseline calculations for measurement and verification of energy and demand savings in a revolving loan program in Texas , 1998 .

[24]  D. Claridge,et al.  A Fourier Series Model to Predict Hourly Heating and Cooling Energy Use in Commercial Buildings With Outdoor Temperature as the Only Weather Variable , 1999 .

[25]  Benjamin Y. H. Liu,et al.  The long-term average performance of flat-plate solar-energy collectors , 1963 .

[26]  Andrew G. Alleyne,et al.  IDENTIFICATION OF BUILDING MODEL PARAMETERS AND LOADS USING ON-SITE DATA LOGS , 2008 .

[27]  Ankit Kumar,et al.  Passive Architectural Design Index applied to vernacular and passive buildings , 2016 .

[28]  David E. Claridge,et al.  REPLACING INEFFICIENT EQUIPMENT --- AN ENGINEERING ANALYSIS TO JUSTIFY PURCHASING A MORE EFFICIENT CHILLER , 2005 .

[29]  David E. Claridge.  A Perspective on Methods for Analysis of Measured Energy Data from Commercial Buildings , 1998 .

[30]  Jie Chen,et al.  Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural n , 2011 .

[31]  Jeffrey D. Spitler,et al.  Applicability of calculation methods for conduction transfer function of building constructions , 2009 .

[32]  Stig Hammarsten,et al.  A critical appraisal of energy-signature models , 1987 .

[33]  T. Agami Reddy,et al.  Development and application of regression models to predict cooling energy consumption in large commercial buildings , 1994 .

[34]  David E. Claridge,et al.  An expert system for building energy consumption analysis: prototype results , 1987 .

[35]  M. M. Gouda,et al.  Low-order model for the simulation of a building and its heating system , 2000 .

[36]  V. Schulz,et al.  Regional energy supply optimization with multiple objectives , 1984 .

[37]  Ahmad Rasdan Ismail,et al.  Computational Analysis of Thermal Building in a No-uniform Thermal Environment , 2015 .

[38]  G. Guyon,et al.  Validation of two French building energy programs. Part 2: Parameter estimation method applied to empirical validation , 1999 .

[39]  Johan Efberg,et al.  YALMIP : A toolbox for modeling and optimization in MATLAB , 2004 .

[40]  Eric Monmasson,et al.  Thermal parameter identification of simplified building model with electric appliance , 2011, 11th International Conference on Electrical Power Quality and Utilisation.

[41]  Henrik Madsen,et al.  Identification of the main thermal characteristics of building components using MATLAB , 2008 .

[42]  Peter R. Armstrong,et al.  Predictive pre-cooling of thermo-active building systems with low-lift chillers , 2011 .

[43]  Xinhua Xu,et al.  A simple time domain calculation method for transient heat transfer models , 2008 .

[44]  Xiaoshu Lü,et al.  Modelling of heat and moisture transfer in buildings: I. Model program , 2002 .

[45]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[46]  Shengwei Wang,et al.  An improvement to frequency-domain regression method for calculating conduction transfer functions of building walls , 2008 .

[47]  Michele De Carli,et al.  A simplified mathematical model for transient simulation of thermal performance and energy assessment for active facades , 2015 .

[48]  Mario Vasak,et al.  Model predictive control of heating and cooling in a family house , 2011, 2011 Proceedings of the 34th International Convention MIPRO.

[49]  D. Bouyssou,et al.  Comparison of two decision-aid models applied to a nuclear power plant siting example , 1986 .

[50]  David E. Claridge,et al.  Generalization of the Fourier Series Approach to Model Hourly Energy Use in Commercial Buildings , 1999 .

[51]  Qi Han,et al.  Distributed wireless control for building energy management? , 2010, BuildSys '10.

[52]  Chris Underwood,et al.  Modelling Methods for Energy in Buildings , 2004 .

[53]  Ari Rabl.  Parameter estimation in buildings: Methods for dynamic analysis of measured energy use , 1988 .

[54]  Stéphane Bertagnolio,et al.  Building and HVAC system simulation with the help of an engineering equation solver , 2008 .

[55]  Margaret F. Fels,et al.  Refraction of PRISM results into components of saved energy , 1986 .

[56]  J. S. Haberl,et al.  Improving energy audits; How annual and monthly consumption data can help , 1990 .

[57]  Youming Chen,et al.  A novel and simple building load calculation model for building and system dynamic simulation , 2001 .

[58]  David E. Claridge,et al.  Ambient-temperature regression analysis for estimating retrofit savings in commercial buildings , 1998 .

[59]  V. Harish,et al.  Demand side management in India: Action plan, policies and regulations , 2014 .

[60]  James E. Braun,et al.  Reducing energy costs and peak electrical demand through optimal control of building thermal storage , 1990 .

[61]  Martti Viljanen,et al.  Analytical Model for Predicting Whole Building Heat Transfer , 2011 .

[62]  Fariborz Haghighat,et al.  A procedure for calculating thermal response factors of multi-layer walls—State space method , 1991 .

[63]  P. Curtiss,et al.  Heating and Cooling of Buildings , 2009 .

[64]  Milan Prodanovic,et al.  Two-stage Optimization for Building Energy Management , 2014 .

[65]  J. Olseth,et al.  A model for the diffuse fraction of hourly global radiation , 1987 .

[66]  G. Mustafaraj,et al.  Development of room temperature and relative humidity linear parametric models for an open office using BMS data , 2010 .

[67]  Andrzej Urbaniak,et al.  Utilization of intelligent control algorithms for thermal comfort optimization and energy saving , 2011, 2011 12th International Carpathian Control Conference (ICCC).

[68]  Nathan Mendes,et al.  Predictive controllers for thermal comfort optimization and energy savings , 2008 .

[69]  Andra Blumberga,et al.  Government and Municipality Owned Building Energy Efficiency System Dynamics Modelling , 2015 .

[70]  Vinod Mubayi,et al.  Modelling energy-economic interactions in developing countries: a linear-programming approach , 1981 .

[71]  Fu Xiao,et al.  A multi-level energy performance diagnosis method for energy information poor buildings , 2015 .

[72]  K. Ng,et al.  Predictive and diagnostic aspects of a universal thermodynamic model for chillers , 1995 .

[73]  António E. Ruano,et al.  Neural networks based predictive control for thermal comfort and energy savings in public buildings , 2012 .

[74]  Jeff Haberl,et al.  The Great Energy Predictor Shootout II : Measuring Retriofit Savings-Overview and Discussion of Results , 1996 .

[75]  Igor Škrjanc,et al.  Theoretical and experimental FUZZY modelling of building thermal dynamic response , 2001 .