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Solar Energy Incorporated Day-lighting Prediction Model Using Hypothetical Module Pongsak Chaisuparasmikul, Raymond J Clark, Robert J Krawczyk College of Architecture Illinois Institute of Technology ISES 2003 Solar World Congress Gvteborg, Sweden June 14-19, 2003.

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slide2
Pongsak Chaisuparasmikul, Raymond J Clark, Robert J Krawczyk

College of ArchitectureIllinois Institute of Technology

ISES 2003 Solar World CongressGvteborg, Sweden June 14-19, 2003.

slide3
KEY ISSUES IN SOLAR ENERGY PREDICTION MODEL
  • Building consumes 35% of total energy consumption.
  • Need the simplified method and tool for solar heating, cooling, and

daylighting during the schematic design process.

  • Modelling becomes the major issue of providing knowledge based information for

designing solar energy efficient buildings.

  • Measurable digital studying of the approximate method for the solar energy.
  • Ability to identify the potential and problems related the functions and parameters.
  • Addressing the issues of development the model into the software.
slide4
ABOUT THIS RESEARCH
  • Looking at the building solar cooling model (which is more important for the office

buildings in the U.S.A.)

  • Finding could be iterative and alternative solutions, during the conceptual design

process.

  • Dealt with the interactive process, many unknown variables, energy approximation

and probability model.

slide5
OBJECTIVES
  • Create the solar cooling prediction models or equations.
  • Find the approximate methods for the measurement of the solar heating, cooling and daylighting.
  • Study the solar energy model without having to use the complicated building energy design software.
  • Providing a mean of simplifying the calculation of solar cooling.
slide6
METHODOLOGY
  • Wrote the source code program to create the DOE-2 input simulation from the nested loop to generate the meaningful data for the multiple parameters.
  • Assessment the influencial data from each of the parameters resulted from the DOE-2 simulation.
  • Sensitivity analysis and correlation methods were used to select the most significant design variables.
  • Regression procedure was using SPSS statistical software tp conduct and identify the principal form of relationships.
  • Data from these selected parameters were generated for developing the multiple non-linear regression models (least square models) that can fit into the linear regression models.
  • Multiple linear regressions were performed to derive the prediction model to obtain the best-fit equations.
slide7
MODULE
  • The module was developed as a simulation input model for the DOE-2 processor.
  • Intelligent module worked as an environmental interaction building (space, size,

envelope materials, temperature, condition), and can be programmed as any the

building types).

  • Module concept was derived from the finite element theory (Raymond J. Clark)
  • Any building forms or geometries can be approximately studied if they were divided into the smallest workable parts.
  • Work well in studying the relative probability of influential parameters that are affected from the solar energy.
slide8
MODULE
  • Module size: 15 ft x 15 ft (4.50 m x 4.50 m) 14ft (4.20 m) floor to floor high,
  • 10 ft (3.00 m) floor to ceiling high.
  • Climate Location: Chicago, Illinois, USA.
  • Weather file: TRY (Test Reference Year)
  • Sample
  • Module schedule used the typical office-building schedule.
  • Module wall, floor, partition, ceiling, and roof used the typical office building: size, material, transmission U-value, insulation.
  • Control strategies: temperature, light (1.2W/m²), daylight, ventilation rate, number of people and equipment.
  • Day-lighting schedule:

From 8.00am-6pm each day into the middle of the window façade, 5 ft (1.52 m) deep to the inside, 3 ft (0.91 m) above the floor.

  • Infiltration rate 0.06 cfm per window perimeter.
slide10
MODULE

Pongsak Chaisuparasmikul, Raymond J Clark, Robert J Krawczyk

College of ArchitectureIllinois Institute of Technology

ISES 2003 Solar World CongressGvteborg, Sweden June 14-19, 2003.

slide11
MODULE

Pongsak Chaisuparasmikul, Raymond J Clark, Robert J Krawczyk

College of ArchitectureIllinois Institute of Technology

ISES 2003 Solar World CongressGvteborg, Sweden June 14-19, 2003.

slide12
DESIGN PARAMETERS
  • Deterministic
  • Variability
slide13
DESIGN PARAMETERS
  • Orientation: 8 orientation;

north, north-east, east, south-east, south, south-west, west, north-west.

  • Fenestration: 4 window to wall ratio

40%, 50%, 60%, 80%.

  • Glass types: 8 glass types;

Monolithic clear & tinted; Insulated clear & tinted; Low-E clear, tinted, green, reflective

  • Overhang shading: 4 overhangs shading ratio;

none, 25%, 50%, 0.75%

  • Fin shading: 4 fins shading ratio;

none, 25%, 50%, 75%

slide15
SIMULATION MODEL
  • Customized input model
  • Interactive and interfacing model with program Front end software
  • Library entry data
  • Programming the model
  • Parametric analysis was assessed to obtain the most influential of each design

variables to the annual solar heating.

slide16
Solar radiation: variation of solar radiation assessment is considered as a probability

Module with

The design parameters

Module as a finite element black box

Customized Input Model

SIM File

SUM File

DOE-2 Simulation

49,135 runs

Regression procedure to identify the principal form of relationships

Sensitivity analysis and correlation method to select the most significant design variables.

Parametric analysis to obtain the most influential variables

Testing the model

Multiple non-linear regression

models fit on linear regression

model

Prediction Model

Form of equations

slide17
Solar radiation: variation of solar radiation assessment is considered as a probability

Module with

The design parameters

Module as a finite element black box

Customized Input Model

SIM File

SUM File

DOE-2 Simulation

49,135 runs

Regression procedure to identify the principal form of relationships

Sensitivity analysis and correlation method to select the most significant design variables.

Parametric analysis to obtain the most influential variables

Testing the model

Multiple non-linear regression

models fit on linear regression

model

Prediction Model

Form of equations

slide18
Solar radiation: variation of solar radiation assessment is considered as a probability

Module with

The design parameters

Module as a finite element black box

Customized Input Model

SIM File

SUM File

DOE-2 Simulation

49,135 runs

Regression procedure to identify the principal form of relationships

Sensitivity analysis and correlation method to select the most significant design variables.

Parametric analysis to obtain the most influential variables

Testing the model

Multiple non-linear regression

models fit on linear regression

model

Prediction Model

Form of equations

slide19
Solar radiation: variation of solar radiation assessment is considered as a probability

Module with

The design parameters

Module as a finite element black box

Customized Input Model

SIM File

SUM File

DOE-2 Simulation

49,135 runs

Regression procedure to identify the principal form of relationships

Sensitivity analysis and correlation method to select the most significant design variables.

Parametric analysis to obtain the most influential variables

Testing the model

Multiple non-linear regression

models fit on linear regression

model

Prediction Model

Form of equations

slide20
Solar radiation: variation of solar radiation assessment is considered as a probability

Module with

The design parameters

Module as a finite element black box

Customized Input Model

SIM File

SUM File

DOE-2 Simulation

49,135 runs

Regression procedure to identify the principal form of relationships

Sensitivity analysis and correlation method to select the most significant design variables.

Parametric analysis to obtain the most influential variables

Testing the model

Multiple non-linear regression

models fit on linear regression

model

Prediction Model

Form of equations

slide21
Solar radiation: variation of solar radiation assessment is considered as a probability

Module with

The design parameters

Module as a finite element black box

Customized Input Model

SIM File

SUM File

DOE-2 Simulation

49,135 runs

Regression procedure to identify the principal form of relationships

Sensitivity analysis and correlation method to select the most significant design variables.

Parametric analysis to obtain the most influential variables

Testing the model

Multiple non-linear regression

models fit on linear regression

model

Prediction Model

Form of equations

slide22
PARAMETRIC ANALYSIS: TREND OF DISTRIBUTION

Solar heating and cooling curve

slide24
PARAMETRIC ANALYSIS: TREND OF DISTRIBUTION

Solar cooling peaks in June, July, or August

Solar heating peaks in December, or January

slide25
SENSITIVITY ANALYSIS AND CORRELATION: SPREE PLOT

** Correlation is significant at the 0.01 level (2-tailed).

slide27
RESULTS

Mean was closer

than median

Histogram

Scattered outliers

Scattered Curve Fit

slide28
RESULTS

Data Residual Identification

Solar cooling

“S” distribution

Solar heating

“S” distribution with a significant on line plotted for both of the prediction model

slide29
RESULTS

Regression Line

R² = 0.21

R² = 0.46 use Variable addition & multiplication

R² = 0.79 use Z transform; Z transform = X - Mean

Estimator of scattered data from regression line

Std = 1 , Mean = 0

Comparison the predicted value and standard residual with DOE-2 simulation

“S” distribution with a significant on line plotted for both of the prediction model

slide30
RESULTS

Regression Model after Z transform

a Predictors: (Constant), Standardized Residual

b Predictors: (Constant), Standardized Residual, Standardized Predicted Value

c Dependent Variable: COOLING

slide31
FORM OF THE MODEL
  • Form of equation was polynomial probability distribution.
  • The variables were transformed (multiplication) one by one, and add new variables into the

equations.

  • Y=(0.254OR+0.065FN)-(0.025OR²+0.004GL²+0.008OH²)+(0.044OR*WR-0.005OR*GL-

0.034OR*OH-0.010OR*FN)-(0.002OR*WR*GL+0.003OR*WR*OH+0.003OR*WR*FN-

0.003OR*GL*OH-0.006OR*OH*FN+0.002GL*OH*FN) ____Least Square Line-1

  • Y=(0.108WR-0.087GL-0.343OH)+(0.023OH*GL)+(0.048OH²-0.003OH²*WR-0.003OH²*GL)

____Least Square Line-2

  • Y=(0.216WR-0.147OH-0.125FN)-(0.141OH*GL) ____Simplified Model

Where Y= Maximum solar heat gain;

OR=Orientation;

WR=Window to wall ratio;

GL=Glass types;

OH= Overhang shading;

FN =Fin shading.

slide32
VALIDATION: TESTING THE MODEL
  • The valid testing of this model was conducted to see how this model was deviated from the
  • standard errors and compared with DOE-2 simulation results
  • The test had “S” distribution with a significant line plotted
  • Y(Model) – Y(DOE-2) : Model deviated from standard error

N-2

slide33
CONCLUSIONS
  • The results were to find the best-predicted value of Y (solar cooling energy) that

respond well to the changing of the combination of design parameters X variables

  • The model was in the form of quadratic and cubic equations
  • Testing of this model was proposed to see how these models were deviated from

the standard error and compared with DOE 2 results.

  • These methods were able to include all of the envelope design parameters.
  • The equation models can be developed to provide the effective simplified tools for

solar cooling.

  • Future research include using these models in the measurement and verifiction of

Midwest Chicago Green Technology Project.

slide34
THANK YOU FOR BEING HERE

FOR MORE DETAILED INFORMATION PLEASE SEE MY PAPER NO. 06 22

OR YOU CAN CONTACT ME AT

EMAIL: [email protected]

WEBSITE: www.iit.edu/~chaipon/

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