An empirical or regression modeling approach is simple to develop and easy to use compared to detailed hourly simulations of energy use in commercial buildings. Therefore, regression models developed from measured energy data are becoming an increasingly popular method for determining retrofit savings or identifying operational and maintenance (O&M) problems. Because energy consumption in large commercial buildings is a complex function of climatic conditions, building characteristics, building usage, system characteristics and type of heating, ventilation, and air conditioning (HVAC) equipment used, a multiple linear regression (MLR) model provides better accuracy than a single-variable model for modeling energy consumption. Also, when hourly monitored data are available, an issue which arises is what time resolution to adopt for regression models to be most accurate. This paper addresses both these topics. This paper reviews the literature on MLR models of building energy use, describes the methodology to develop MLR models, and highlights the usefulness of MLR models as baseline models and in detecting deviations in energy consumption resulting from major operational changes. The paper first develops the functional basis of cooling energy use for two commonly used HVAC systems: dual-duct constant volume (DDCV) and dual-duct variable air volume (DDVAV). Using these functional forms, the cooling energy consumption in five large commercial buildings located in central Texas were modeled at monthly, daily, hourly, and hour-of-day (HOD) time scales. Compared to the single-variable model (two-parameter model with outdoor dry-bulb as the only variable), MLR models showed a decrease in coefficient of variation (CV) between 10 percent to 60 percent, with an average decrease of about 33 percent, thus clearly indicating the superiority of MLR models. Although the models at the monthly time scale had higher coefficient of determination (R2) and lower CV than daily, hourly, and HOD models, the daily and HOD models proved more accurate at predicting cooling energy use.

Issue Section:
Research Papers
Topics:
Modeling, Energy consumption, Cooling, Structures, Ducts, HVAC equipment, Regression models, Air conditioning, Heating, Maintenance, Resolution (Optics), Simulation, Ventilation
1.
Abushakra, B., Zmeureanu, R., and Fazio, P., 1995, “Evaluation of Conventional Inverse Models For Predicting Electricity Demand of A Large Institutional Building,” Proceedings of the Second International Conference on Indoor Air Quality, Ventilation, and Energy Conservation in Buildings, May 9–12, Montreal, Canada, pp. 559–566.
2.
Boonyatikam, S., 1982, “Impact of Building Envelopes on Energy Consumption and Energy Design Guidelines,” Proceedings of the ASHRAE/DOE Conference on Thermal Performance of the Exterior Envelope of Buildings II, pp. 469.
3.
Bronson
D.
,
Hinchey
S.
,
Haberl
J. S.
,
O’Neal
D. L.
, and
Claridge
D. E.
,
1992
, “
A Procedure for Calibrating the DOE-2 Simulation Program to Non-Weather Dependent Measured Loads
,”
ASHRAE Transactions
, Vol.
98
, Part 1, pp.
636
–
652
.
4.
Copeland
C.
,
1983
, “
Retrofit Energy Studies Using the DOE-2 Computer Simulation Program
,”
ASHRAE Transactions
, Vol.
89
, Part
1A
, pp.
341
–
351
.
5.
Daniel, C., and Wood, F. S., with assistance of Gorman, J. M., 1980, Fitting Equations to Data: Computer Analysis of Multifactor Data, 2nd ed., John Wiley and Sons, New York, NY.
6.
Dhar, A., Reddy, T. A., and Claridge, D. E., 1994, “Improved Fourier Series Approach to Modeling Hourly Energy Use in Commercial Building,” Proceedings of the 1994 ASME/JSES/JSES International Solar Energy Conference, San Francisco, March 27–30, pp. 455–468.
7.
Diamond
S. C.
, and
Hunn
B. D.
,
1981
, “
Comparison of DOE-2 Computer Program Simulations to Metered Data For Seven Commercial Buildings
,”
ASHRAE Transactions
, Vol.
87
, Part 1, pp.
1222
–
1231
.
8.
Draper, N., and Smith, H., 1981, Applied Regression Analysis, 2nd ed., John Wiley and Sons, New York, NY.
9.
Fels, M. (Ed), 1986, “Special Issue Devoted to Measuring Energy Savings, The Princeton Scorekeeping Method (PRISM),” Energy and Buildings, Vol. 9, No. 1 and 2.
10.
Haberl
J. S.
, and
Claridge
D. E.
,
1987
, “
An Expert System for Building Energy Consumption Analysis; Prototype Results
,”
ASHRAE Transactions
, Vol.
93
, Part 1, pp.
979
–
998
.
11.
Kaplan, M., Jones, B., and Jansen, H., 1992, “DOE2.1C Model Calibration With Monitored End-Use Data,” The ACEEE 1992 Summer Study on Energy Efficiency in Buildings, pp. 10.115–10.125.
12.
Katipamula, S., Reddy, T. A., and Claridge, D. E., 1995a, “Bias in Predicting Annual Energy Use in Commercial Building With Regression Models Developed From Short Data Sets,” Proceedings of the 1995 ASME/JSES/JSES International Solar Energy Conference, Maui, Hawaii, pp. 99–110.
13.
Katipamula, S., Reddy, T. A., and Claridge, D. E., 1995b, “Effect of Time Resolution on Statistical Modeling of Cooling Energy Use in Large Commercial Buildings,” ASHRAE Transactions, Vol. 101, Part 2.
14.
Katipamula, S., Reddy, T. A., and Claridge, D. E., 1994, “Development and Application of Regression Models to Predict Cooling Energy Consumpdon in Large Commercial Building,” Proceedings of the 1994 ASME/JSES/JSES International Solar Energy Conference, San Francisco, March 27–30, pp. 307–322.
15.
Katipamula
S.
, and
Claridge
D. E.
,
1993
, “
Use of Simplified System Models to Measure Retrofit Energy Savings
,”
ASME JOURNAL OF SOLAR ENERGY ENGINEERING
, Vol.
116
, No.
2
, May, pp.
57
–
68
.
16.
Katipamula
S.
, and
Claridge
D. E.
,
1992
, “
Monitored Air Handler Performance and Comparison With A Simplified System Model
,”
ASHRAE Transactions
, Vol.
98
, Part 2, pp.
341
–
351
.
17.
Kissock, J. K., Haberl, J. S., Claridge, D. E., and Reddy, T. A., 1992, “Measuring Retrofit Savings for the Texas LoanSTAR Program: Preliminary Methodology and Results,” Proceedings of the ASME/JSES/KSES International Solar Energy Conference, Maui, Hawaii, pp. 299–308.
18.
Kissock, J. K., Reddy, T. A., Fletcher, D., and Claridge, D. E., 1993, “The Effect of Short Data Periods on The Annual Prediction Accuracy of Temperatures– Dependent Regression Models of Commercial Buildings Energy Use,” Proceedings of the 1993 ASME/ASES/SED International Solar Energy Conference, Washington, D.C., April, pp. 455–463.
19.
Knebel, D. E., 1983, “Simphfied Energy Analysis Using the Modified Bin Method,” American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc., Atianta, GA.
20.
Leslie
N. P.
,
Aveta
G. A.
, and
Sliwinski
B. J.
,
1986
, “
Regression Based Process Energy Analysis System
,”
ASHRAE Transactions
, Vol.
92
, Part 1A, pp.
93
–
102
.
21.
Liu, M., and Claridge, D. E., 1995, “Application of Calibrated HVAC System Models to Identify Component Malfunctions and to Optimize Operation and Control Schedules,” Proceedings of the 1995 ASME/JSME/JSES International Solar Energy Conference, Vol. 1, Maui, Hawaii, March 19–24, 1995, pp. 209–217.
22.
MacDonald, M., 1988, “Power Signatures as Characteristics of Commercial and Related Buildings,” Fifth Annual Symposium on Improving Building Energy Efficiency in Hot and Humid Climates, Energy Systems Laboratory, Texas A&M University, College Station, Texas, pp. 80–87.
23.
Mazzucchi
R. P.
,
1986
, “
The Project of Restaurant Energy Performance End-Use Monitoring and Analysis
,”
ASHRAE Transactions
, Vol.
92
, Part 2B, pp.
328
–
349
.
24.
Rachlin, J., Fels, M., and Socolow, R., 1986, “The Stability of PRISM Estimates,” Energy and Buildings, Vol. 9, No. 1 and 2.
25.
Reddy
T. A.
,
Katipamula
S.
,
Kissock
J. S.
, and
Claridge
D. E.
,
1995
, “
The Functional Basis of Steady-State Thermal Energy Use in Air-Side HVAC Equipment
,”
ASME JOURNAL OF SOLAR ENERGY ENGINEERING
, Vol.
117
, No.
1
, pp.
31
–
39
, February.
26.
Reddy
T. A.
, and
Claridge
D. E.
,
1994
, “
Using Synthetic Data to Evaluate Multiple Regression and Principal Component Analysis for Statistical Models of Daily Energy Consumption
,”
Energy and Buildings
, Vol.
21
, pp.
35
–
44
.
27.
Ruch
D.
, and
Claridge
D. E.
,
1992
, “
A Four Parameter Change-Point Model for Predicting Energy Consumption in Commercial Buildings
,”
ASME JOURNAL OF SOLAR ENERGY ENGINEERING
, Vol.
114
, No.
2
, pp.
77
84
, February.
28.
Schrock, D. W., and Claridge, D. E., 1989, “Predicting Energy Usage in a Supermarket,” Proceedings of the Sixth Annual Symposium on Improving Building Systems in Hot and Humid Climates, Dallas, Texas, October 3–4, 1989, pp. F19–F27.
29.
Seem
J. E.
, and
Braun
J. E.
,
1991
, “
Adaptive Methods for Real-Time Forecasting of Building Electric Demand
,”
ASHRAE Transactions
, Vol.
97
, Part 1, pp.
710
–
721
.
30.
Sullivan
R. T.
, and
Nozaki
S. A.
,
1984
, “
Multiple Regression Techniques Applied to Fenestration Effects On Commercial Building Energy Performance
,”
ASHRAE Transactions
, Vol.
90
, Part
1A
, pp.
116
–
123
.
31.
Vandon, M., Kreider, J. S., and Norford, L. K., 1991, “Improvement of the Solar Calculations in the Modified Bin Method,” ASHRAE Transactions, Vol. 97, Part 2.
This content is only available via PDF.
Copyright © 1998
 by The American Society of Mechanical Engineers
You do not currently have access to this content.