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Saved by Brian Matthews
on November 2, 2008 at 10:54:23 pm
 

Energy Modeling of Buildings

 

Description:

     Energy modeling of buildings is a practice of predicting the energy and fuel use of a building.             

 

Purpose:

     The goal of energy modeling is to accuratly predict the energy use of a particular building to either test the energy performance of the building with regards to established codes and standards, or to compair and contrast two buildings in order to find the resulting savings from the application of prospective energy efficient measures.    Only one building is built or improved, so to establish the ammount that the building has been improved, modeling is required to establish the difference. 

 

Industries served:

     All businesses, residential establishements, and industrial buildings that have a significant utility or energy use, would benefit from energy modeling due to its ability to predict the useful savings that result from utilizing more efficient mechanical equipment or energy efficient practices.   Not all energy efficient equipment or practices fit all situations.   Analysis is necessary to find the most worthwhile savings created in exchange for the typically higher up-front cost and effort. 

 

Methods:

     Classification of the different methods are diverse in their practice but generally subscribe to the goal of predicting energy    use.   They range from the most complext of Computational Fluid Dynamic (CFD) simulations and hourly, or subhourly     simulations to simple spreadsheets that approximate the conditions of a particular site, energy use, occupancy, and     various other factors.  The choice between the different methods is primarily based on level of accuracy desired and what    methods codes or governing body require.    

 

Techniques:

     Techniques of modeling a particular building with the unique characteristics of that building is strongly dependent on the modeling software used.    Most software is very adaptable from one building to the next but the individual techniques of modeling HVAC equipment in particular can be very detailed.   Understanding how commons air systems work in a building are essential to making a properly working model.    

 

Existing Guides

  • Equest Workbook
  • MPP Guidebook

 

Common Units Used in energy modeling of buildings.

  •   Tons of cooling
  •   mmbtu vs. mbtu vs. kbtu
  •   Therm
  •   cooling horsepower
  •   cfg (Cubic Foot of Natural Gas)
  •   Killowatt-hours (kWh), Killowatts (kW), Kvar,  Phase angle, 3phase vs. 2 phase vs. single phase AC power
  •   Square foot floor area approximation vs. actual flooring survace not including all thickness of walls.
  •   Azmuth  

Common Words used in Energy Modeling

 

 

Software:

 

 

Data Classification:

 

     First, a few words on numbers, and the different types of numbers used in energy modeling:

     Below are some of the main types of numbers used in building modeling:

  1. SES "Scalar Evenly Scheduled" -  A number that is applied to a building parameter independent of schedules because in the calculation used to attain this number there had been predetermined hours per day of use assumed.  An example of this would be a an amount of water outdoor plants need per year at a particular building.   For simplicity, it is easier to assume a 5,000 gallons per year and thereby 50,000/ 365 days =137 gallons per average day, than to calculate seasonal rainfall, hourly evaporation etc.   SES numbers are highly volatile and subject to error if not used properly.  "SES" numbers do have a certain advantage in certain situations so long as their interaction and affect on accuracy of the model are accounted for.  
  2. SDS "Scalar Definitively Scheduled" -A number that is only valid when used in conjunction with a schedule where every hour of every day has a value ranging from 1-100% (or fraction from 0.01 to 1.0).    An example of this is a 60 watt light bulb used 2.4 hours a day.   The load will never be 60 watts 24/7.  
  3. ISV  "Isolated Static Values"   These are numbers that never change regardless of schedule, part load ratios, or other variable reactionary events.  The gallons per minute of a shower head is an example.  
  4. IDV  "Interactive Dependent Values"  
    • On a building input, these type of numbers must be accompanied by appropriate pairs or in groups in order to run a working simulation.    If, for example, only 4 out of 5 numbers are correct for an HVAC system, but one number is either not known or incorrect, the simulation results may be volatile in accuracy.  Often, when a modeler overrides features of a program intended to be user-friendly (and thereby rigid and inadaptable) a volatility in the results may occur because the automatic features of the program were unintentionally prevented to run their course.    Sometimes you must either know 5-6 critical numbers or be willing to accept the resulting limitations of only being able to input 1-2 numbers (and thereby rely on the rigid automatic features of the software) This situation can be referred to as "Trash in Trash out". 
    • On a building's Output side of things,  there are a limited quantity of energy values yeilding whether a building is of high or low energy performance.    Each one of the energy values are either directly affected by one energy measure or joint affected by many energy measures stacked on each other.   Errors can occur, but not be seen, if one measure's over estimation of energy savings is masked by another energy measures under estimation of energy savings.   All energy modeling requires a fine combing of the data to check that all is in order.        
  5. CKV "Critical Key Values"  Just as there are potentially many dozens of inputs that go to make up a model, there is an accompanying importance some numbers over others.   From most numbers an error range of 4-8% may be acceptable, but if key values have such a loose tolerance then this may lead to 10 or more percent difference on the result.   Some numbers magnify their loose tolelrances while other numbers have a reduced impact on the overall result accuracy.    Doing approximations of certain numbers should be done with proper care as their impact on the model's results may not be apparent in first runs of a building model.     

 

Fundamental building characteristics.

  • Outside Air
    • Air Changes Per Hour
    • HVAC exhaust-supply offsets
    • Wind 
    • Cracks in building envelopes
    • Stack effect through all Elevator,mechanical shafts, and stairwells.
    • Air buoyancy effect through all elevator, mechanical shafts, and stairwells.
  • HVAC
    • CVCT systems
    • VAV systems
    • PTAC systems
    • Typical residential systems (urban)
    • Typical industrial systems
    • ASHRAE SYSTEM TYPES VS PROGRAM INPUTS
  • ENVELOPE
    • Thermal bridging
    • Exposed floor slab, balconies
    • Roof dynamics
    • Cavity wall physics
    • Moisture barrier
    • Wall construction type dependencies on local climate.
  • Occupancy
    • Occupancy Schedules:
      • Residential
      • Health Care facilities

 

Baseline Building Information

  • Modeling Codes
    • ASHRAE 90.1 2004 APPENDIX G
    • MPP
    • LEED analysis methodology (per Credit)
  • Typical Building Energy Use Resources
    • EBECS
    •  

Improved Building Information

  • Modeling Codes
    • ASHRAE 90.1 2004 APPENDIX G
  • Energy Conservation Measure Modeling Approaches
    • SOLAR PV
    • CONDENSING BOILERS
    • ENVELOPE IMPROVEMENTS
    • REFRIGERATED STORAGE FACILITY
    • ENVELOPE
    • LIGHTING
    • PROCESS IMPROVEMENT
    • BUILDING MANAGEMENT SYSTEMS
    • ENERGY & HEAT RECOVERY
    • COMBINED HEAT AND POWER (CHP)
    • NATURAL GAS RECRIPROCATING ENGINE GENERATOR, CHILLER
    • BIODESIL RECRIPROCATING ENGINE GENERATOR/ CHILLER
    • ABSORBTION CHILLER
    • HVAC IMPROVEMENT
    • ELEVATOR AND PEOPLE MOVING APPARATUS
    • COMPUTERS, OFFICE EQUIPMENT
    • DAY LIGHTING
    • WIND
    • GROUND-SOURCE GEOTHERMAL
    • RADIANT FLOOR
    • OCCUPANCY SENSING LIGHTING, HVAC
    • EXHAUST AIR DAMPERS
    • EXHAUST CONTROLS

 

Relating what is modeled to Realilty: 

     Overview of information available:

  • Energy Bills
    • Strongly dependent on particular situations.   The energy use of a building is depenedent on the real life occupancy, use of the buildng, and changing weather from year to year.   Such variability leads to numberous notes that must accompany any energy bill number, but seldome does.   Energy bill data compiled over many buildings helps even out some of the discrepencies and there are several resources that have done this:
    • Buildingsdatabook
    •  
  • Typical heating loads by building type, Heating factor calculation
  • Typical cooling loads by building type
  • Typical hot water use for: apartments, restaurants, swimming pools, car wash
  • Typical electrical use for: battery cart charging stations, data centers, Sauna, Steam room, Movie theater                     projection room, etc.

 

 

 

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