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Lecture Objectives:

Lecture Objectives:. Learn about detailed vs. empirical modeling Use life-cycle cost analysis integrated in eQUEST. Whole building modeling. Load System Plant Model. Building. Heating/Cooling System. Plant. Q Building. Q Systems.

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Lecture Objectives:

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  1. Lecture Objectives: • Learn about detailed vs. empirical modeling • Use life-cycle cost analysis • integrated in eQUEST

  2. Whole building modeling Load System Plant Model Building Heating/Cooling System Plant QBuilding QSystems

  3. Example of System Models:Schematic of simple air handling unit (AHU) Mixing box m - mass flow rate [kg/s], T – temperature [C], w [kgmoist/kgdry air], r - recirculation rate [-], Q energy/time [W]

  4. Energy and mass balance equations for Air handling unit model – steady state case 1) The energy balance for the mixing box is: ‘r’ is the re-circulated air portion, TO is the outdoor air temperature, TM is the temperature of the air after the mixing box. The air-humidity balance for the mixing box is: wOis the outdoor air humidity ratio and wM is the humidity ratio after the mixing box 2) The energy balance for the heating coil is given as: The energy balance for the cooling coil is given as:

  5. Example of Plant Models:Chiller P electric () = COP () x Q cooling coil () TOA What is COP for this air cooled chiller ? T Condensation = TOA+ ΔT Evaporation at 1oC TCWS=5oC TCWR=11oC water Building users (cooling coil in AHU) COP is changing with the change of TOA

  6. Chiller model: COP= f(TOA , Qcooling , chiller properties) Chiller data: QNOMINAL nominal cooling power, PNOMINAL electric consumption forQNOMINAL The consumed electric power [KW] under any condition Available capacity as function of evaporator and condenser temperature Cooling water supply Outdoor air Full load efficiency as function of condenser and evaporator temperature Efficiency as function of percentage of load Percentage of load: The coefficient of performance under any condition:

  7. How to evaluate the whole building simulation tools Two options: • Comparison with the experimental data - monitoring - very expensive - feasible only for smaller buildings 2) Comparison with other energy simulation programs - for the same input data - system of numerical experiments - BESTEST

  8. Comparison with measured data Cranfield test rooms (from Lomas et al 1994a)

  9. BESTEST Building Energy Simulation TEST • System of tests (~ 40 cases) - Each test emphasizes certain phenomena like external (internal) convection, radiation, ground contact • Simple geometry • Mountain climate COMPARE THE RESULTS

  10. Example of best test comparison

  11. Empirical model Load vs. dry bulb temperature Measured for a building in Syracuse, NY Model For average year use TMY2 =835890ton hour = 10.031 106 Btu

  12. What are the reasons for energy simulations? • System Development • Building design improvement • Economic benefits • Budget planning

  13. Life Cycle Cost Analysis • Engineering economics

  14. Life Cycle Cost Analysis • Engineering economics • Compound-amount factor (f/p) • Present worth factor value (p/f) • Future worth of a uniform series of amount (f/a) • Present worth of a uniform series of amount (p/a) • Gradient present worth factor (GPWF)

  15. Parameters in life cycle cost analysis Beside energy benefits expressed in $, you should consider: • First cost • Maintenance • Operation life • Change of the energy cost • Interest (inflation) • Taxes, Discounts, Rebates, other Government measures

  16. Example • Using eQUEST analyze the benefits (energy saving and pay back period) of installing - low-e double glazed window - variable frequency drive in the school building in NYC

  17. What are the reasons for energy simulations? • System Development • Building design improvement • Economic benefits • Budget planning

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