Computer-Aided Energy Analysis In Buildings: An Assessment Of Its Value For Students Of Engineering And Architecture

Date of Award




Degree Name

Doctor of Arts (D.A.)


Purpose. This study determined if computer-aided energy and analysis in buildings would enhance a student's comprehension and prediction accuracy of energy consumption in buildings. There are several public and private computer programs available to predict energy consumption in buildings. Most of them are quite involved and include a detailed economic analysis. A search was conducted for a student-oriented program to use in this study, but a suitable one was not found. Therefore, the author developed a program as part of the research.An interactive program was created and especially designed for use by students. Called Building Energy Analysis Program for Students, BEAPS, it used the Bin Method technique in a block load format. After the student had become familiar with the input routine, the run time was about ten minutes. Program accuracy has been about (+OR-)15 percent of actual metered data for single zone buildings. The accuracy was also confirmed by another computer program.Investigative Procedure. The experimental method was a posttest-only control group design. The independent variables were manual analysis and computer-aided analysis. The dependent variables were prediction accuracy and comprehension.During the Fall of 1979, the author taught Environmental Control Systems in buildings to 35 students. After covering HVAC, energy analysis, and the Bin Method, the class was randomly divided into two groups: Experimental and Control. The Control group took an examination to test their prediction accuracy and comprehension. They were allowed to use only pocket calculators. The Experimental group took the same examination, but they were allowed to use the BEAPS program. Cheating incentive was removed by not making the examination results part of the course grade.The evaluation instrument consisted of a case study of a building with all calculations presented for a Bin Method analysis. At the end of the study the student was presented with three changes in the original building, and was asked to evaluate the effect of the changes for heating and cooling energy consumption. The answer to each change had to be given in two forms. One was a numerical answer (prediction accuracy) of the new energy demanded by the change. The second answer was a multiple choice (comprehension) of whether the change increased, left unchanged, or decreased the energy demand. The student was told that the two answers could be considered separate and independent. Either one could be answered, in any particular order, and the answers did not have to logically follow if the student did not believe their calculations.Findings. There were 13 students in the Experimental group (computer-aided) and 17 students in the Control group (manual). A statistical comparison of the grade point averages of the two groups yielded no significant difference at the 0.01 level (t = -0.02, df = 27.96, ns). One point was awarded for each correct answer; zero points for each incorrect. Statistical analysis was accomplished using the T-TEST Subroutine of the Statistical Package for the Social Sciences, SPSS. For the variable prediction accuracy, the mean raw score for the Experimental group was 3.08 and for the Control group was 0.59. The difference was significant at the 0.01 level (t = -4.07, df = 13.54, p < 0.01). For the variable comprehension, the mean raw score for the Experimental group was 5.00 and for the Control group was 2.35. The difference was significant at the 0.01 level (t = -5.17, df = 24.60, p < 0.01).Conclusions. It was concluded that computer-aided energy analysis improved engineering and architecture students' comprehension and prediction accuracy of energy consumption in buildings. It was also confirmed that an accurate building energy analysis program could be developed for student use.


Engineering, Mechanical; Energy

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