Solar Energy Incorporated Day-lighting Prediction Model Using Hypothetical Module

1. Solar Energy Incorporated Day-lighting Prediction Model Using Hypothetical Module

2. Pongsak Chaisuparasmikul, Raymond J Clark, Robert J Krawczyk College of ArchitectureIllinois Institute of Technology ISES 2003 Solar World CongressGvteborg, Sweden June 14-19, 2003.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. MODULE

10. 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.

11. 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.

12. DESIGN PARAMETERS • Deterministic • Variability

13. 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%

15. 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.

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. PARAMETRIC ANALYSIS: TREND OF DISTRIBUTION Solar heating and cooling curve

23. PARAMETRIC ANALYSIS: TREND OF DISTRIBUTION Solar daylighting curve

24. PARAMETRIC ANALYSIS: TREND OF DISTRIBUTION Solar cooling peaks in June, July, or August Solar heating peaks in December, or January

25. SENSITIVITY ANALYSIS AND CORRELATION: SPREE PLOT ** Correlation is significant at the 0.01 level (2-tailed).

26. SENSITIVITY ANALYSIS AND CORRELATION: SPREE PLOT Significant 5 variables

27. RESULTS Mean was closer than median Histogram Scattered outliers Scattered Curve Fit

28. RESULTS Data Residual Identification Solar cooling “S” distribution Solar heating “S” distribution with a significant on line plotted for both of the prediction model

29. 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

30. RESULTS Regression Model after Z transform a Predictors: (Constant), Standardized Residual b Predictors: (Constant), Standardized Residual, Standardized Predicted Value c Dependent Variable: COOLING

31. 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.

32. 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

33. 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.

34. THANK YOU FOR BEING HERE FOR MORE DETAILED INFORMATION PLEASE SEE MY PAPER NO. 06 22 OR YOU CAN CONTACT ME AT EMAIL: chaipon@iit.edu WEBSITE: www.iit.edu/~chaipon/