Modeling and simulation of a simple building energy system

A mathematical model for building energy systems (BES) is developed which maps the energy transfer processes occurring within the building space. Construction elements making up the building space and the heating and cooling plant responsible for thermal comfort of the occupants are also modeled. This involved quantification of linkages between temperature and humidity conditions and level occupancy (number of occupants, occupancy schedule) within building space. Thermal energy transfer processes of conductive, convective, and radiative heat balance for each surface of the construction elements and a convective heat balance for the building space are modeled. Building space zone is modelled for both sensible and latent thermal energy transfer. State space approach is used to model the building construction elements such as walls, with the parameters estimated using a nonlinear time invariant optimization algorithm with constraints. HVAC system is modelled with a control valve, heat emitter, occupancy driven ventilation controlled through a PID controller. A complete building energy system (BES) modeling procedure based on first principles of building physics is presented. BES model is simulated using MATLAB/Simulink and the results depict the temperature variations within the building space at less computational times.

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

[2]  Refrigerating.  ASHRAE handbook of fundamentals , 1967 .

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

[4]  Burcin Becerik-Gerber,et al.  A model calibration framework for simultaneous multi-level building energy simulation , 2015 .

[5]  Arun Kumar,et al.  A review on modeling and simulation of building energy systems , 2016 .

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

[7]  Massimiliano Manfren,et al.  Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation , 2013 .

[8]  Xiaoshu Lü,et al.  A novel dynamic modeling approach for predicting building energy performance , 2014 .

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

[10]  Arun Kumar,et al.  Planning and implementation strategy of Demand Side Management in India , 2014, 2014 First International Conference on Automation, Control, Energy and Systems (ACES).

[11]  Cara R. Touretzky,et al.  Building-level power demand forecasting framework using building specific inputs: Development and applications , 2015 .

[12]  Kevin M. Smith,et al.  Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy , 2014 .

[13]  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).

[14]  Arno Schlueter,et al.  Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts , 2015 .

[15]  Andrea Costa,et al.  Building operation and energy performance: Monitoring, analysis and optimisation toolkit , 2013 .

[16]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[17]  J.L.M. Hensen.  Energy simulation in building design , 1992 .

[18]  Tao Lu,et al.  Modeling and forecasting energy consumption for heterogeneous buildings using a physical -statistical approach , 2015 .

[19]  Benjamin Engel,et al.  Energy Simulation In Building Design , 2016 .