Baselining Methodology for Facility-Level Monthly Energy Use Part 2: Application to Eight Army Installations

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1 ESL-PA-97/O7-O2 BN (4090) Baselining Methodology for Facility-Level Monthly Energy Use Part 2: Application to Eight Army Installations T. Agami Reddy, Ph.D. Member ASHRAE Namir F. Saman, Ph.D., P.E. MemberASHRAE David E. Claridge, Ph.D., P.E. MemberASHRAE JeffS. Haberl, Ph.D., P.E. MemberASHRAE ABSTRACT W. Dan Turner, Ph.D., P.E. A companion paper discusses various issues involved in baselining energy use at either the whole facility level or at the building level when monthly utility bills are available. It describes the variable-base degree-day (VBDD) and the monthly mean temperature (MMT) regression modeling approaches for normalizing energy use for changes in outdoor temperature from the baseline year to subsequent years, and it proposes statistical equations to determine prediction uncertainty for individual monthly and annual time intervals for change-point energy models whose residual variance is not constant. The objectives of this paper are to apply these concepts to monthly utility electricity and gas use data from eight Army installations nationwide, to evaluate the VBDD and the MMT approaches, and to discuss salient results of the analysis. Finally, we illustrate the use of a statistical method suggested in the companion paper whereby approximate read dates of the utility billing period can be identified in cases where such information is lacking. BACKGROUND AND OBJECTIVES A companion paper (Reddy et al. 1997) suggests that in order to detect the effectiveness of energy efficiency and operation and maintenance (O&M) measures at the whole-facility or whole-building level over a series of years, the observed energy use should be normalized for changes in weather, conditioned floor area, occupancy levels, and connected loads that are not associated with the energy conservation retrofit. In case one wishes to correct for such changes, fractional changes in energy use of a future year as compared to the baseline year could be done as described in Reddy et al. (1997). The effects of weather normalizing are well known, and the companion paper (Reddy et al. 1997) describes two energy software packages, one based on the variable-base degree-day (VBDD) method (Fels et al. 1995) and the other on the monthly mean temperature (MMT) Alan T. Chalifoux, P.E. Member ASHRAE method (Kissock et al. 1994). Normalizing for changes in conditioned building area is straightforward if the entire analysis is performed on a per-unit-area basis as adopted in this study. Normalizing energy use for changes in population is not simple since a relationship must be statistically determined from data already available from similar types of buildings and facilities. Unfortunately, such types of relationships are lacking in the published literature. Energy use in a building, for example, does not necessarily double if the number of occupants is doubled, so a simple proportional relationship is unlikely. Until such time that researchers establish such statistical relationships for different types of buildings and facilities, it would be judicious to simply assume that normalizing energy use by conditioned area also implicitly normalizes for population or occupancy changes. Also, there seems to be no standard method for correcting energy use for connected load increases (often referred to as "creep" by energy managers). That issue is beyond the scope of this study. The companion paper also presents the functional forms of the regression models on which the VBDD and MMT approaches are based and points out that change-point models often suffer from nonuniform model residual behavior. Statistical equations to predict the 90% prediction interval (PI) bands due to model uncertainty and error in the measuring instrument also are proposed in the companion paper. The ability to determine realistic uncertainty bands is an issue of key importance if one wishes to reach statistically meaningful conclusions regarding year-to-year changes in observed energy use. The methodology described in the companion paper has been developed specifically for use as a screening tool for detecting changes in future utility bills and also to track/ evaluate the extent to which Presidential Executive Order 12902, mandating a 30% decrease in energy utility bills from 1985 to 2005, is being met (USACERL 1993). The objectives of this paper are as follows. 1. To evaluate which of the two energy modeling software packages the one based on the VBDD method (Fels et al. 1995) or the one based on the MMT approach (Kissock et T. Agami Reddy is assistant director, Namir F. Saman is a research engineer, David E. Claridge and JeffS. Haberl are associate directors, and W. Dan Turner is director of the Energy Systems Laboratory of the Texas Engineering Experiment Station, Texas A&M University, College Station. Alan T. Chalifoux is a project manager at the U.S. Army Construction Engineering Research Laboratories, Champaign, III. THIS PREPRINT IS FOR DISCUSSION PURPOSES ONLY, FOR INCLUSION IN ASHRAE TRANSACTIONS 1997, V Pt. 2. Not to be reprinted in whole or in part without written permission of the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., 1791 Tullie Circle, NE, Atlanta, GA Opinions, findings, conclusions, or recommendations expressed in this paper are those of the author(s) and do not necessarily reflect the views of ASHRAE. Written questions and comments regarding this paper should be received at ASHRAE no later than July 18,1997.

2 al. 1994) is more appropriate for modeling energy use at the whole-campus or facility level that has buildings of mixed applications (residences, commercial buildings, hospitals, food services, etc.). Utility data that have undergone some sort of "reality check" have been used for this purpose. Fort Hood, a large Army installation located in central Texas, was chosen in view of the fact that extensive data gathering and analysis have been done on this base over the years and a comprehensive report on utility and services data was available (USACERL 1993). 2. To illustrate the use of a statistical method suggested in the companion paper whereby approximate dates for utility bill read dates can be identified in cases when such information is lacking. 3. To illustrate the use of the baseline models for screening purposes at the monthly level in order to help the energy manager evaluate/assess the effect of certain energy-efficient measures that have been performed. For example, the energy office at Fort Hood, Texas, instituted a demandshedding initiative via frequency-modulated (FM) cycling of residential air-conditioning units (the "FM load management system"). The baseline models are used to discern the effect of this conservation program. 4. To apply/evaluate the baselining methodology to utility data from eight additional Army bases from various parts of the country. The utility data and other relevant data, such as base area and population needed for the analysis, were downloaded from the central Defense Energy Information System (DEIS) database. One could not expect such data to be as "clean" as those from the Fort Hood Army base since the former usually are not subjected to careful "reality checks" before being entered into the database. Further, unlike the data used for number 1 above, where daily mean outdoor temperature data for Temple, Texas (a town close to Fort Hood), were available for the analysis, we used monthly mean temperature data from the National Climatic Data Center (NCDC n.d.) at Asheville, North Carolina. It was the intent of this study to evaluate whether baseline models identified from "general" sources such as the DEIS and NCDC databases are appropriate to use as screening tools for detecting changes in future utility bills. COMPARISON OF VBDD AND MMT COMPUTER SOFTWARE PACKAGES Fort Hood is a large army base located in central Texas, about 70 miles north of Austin. It has a daytime population of approximately 65,000 that shrinks to 40,000 at night. Its building stock is diverse, totaling more than 5,200 individual buildings and covering about 25.5 million square feet, 8.5 million of which is family housing. Fort Hood's utility bills for fiscal year 1993 totaled $16 million for electricity and $5 million for natural gas. The base is composed of three separate and physically distinct cantonment areas: Main Fort Hood, West Fort Hood, and North Fort Hood. The building stock of Main Fort Hood covers about 23.6 million square feet (91% of the total). West Fort Hood is located four miles west of Main Fort Hood and contains about 1.4 million square feet of buildings (5.4% of the total). North Fort Hood, located 20 miles north of Main Fort Hood, is composed of about 0.82 million square feet of buildings (3.2% of the total), most of which are occupied only during the summer months when National Guard training is in progress. Approximately 500 buildings scattered throughout the three cantonment areas are individually metered for electric power consumption. Utility electric power to Fort Hood is metered in three locations: Main Fort Hood, West Fort Hood, and North Fort Hood, where separate substations have been installed. Natural gas is metered in only two locations: one gas meter records the combined gas usage of the Main and West cantonment areas, and the other gas meter records usage at North Fort Hood. Water metering is similar to gas metering: one meter for Main and West combined, and another for North Fort Hood only. According to the Fort Hood energy office, the dates when the utility meters are read correspond to calendar months to within two or three days. VBDD and MMT software were used to identify monthly models for electricity use, electric demand, gas use, and water use for each of the three cantonment areas on a yearly basis for all years from 1989 to Complete details regarding the comparison are given in Saman et al. (1995) and Reddy et al. (1996a). A concise visual comparison of the performance of both software packages is provided by Figure 1, which assembles the values of the coefficient of variation of the root-meansquare error (CV-RMSE) for all four utilities (electricity use, electric demand, natural gas, and water use) for all years and for all the models for the Main cantonment area only. Note that while we have only used the 3P modeling capability of the MMT package, we have used all the models offered by the VBDD software (CO cooling only, HO heating only, HC heating and cooling). From Figure 1, we note that in most cases, the MMT model performs better than the VBDD model, and even in the few cases where it did not, the difference was very little. The reason for this phenomenon is unclear and could be partly due to VBDD models being more sensitive than MMT models to the two- to three-day discrepancy between the dates on which the utility meters are read and calendar month periods. Another possible cause could be that VBDD models are most appropriate for shell-dominated buildings such as residences. Because housing only constitutes about 25% of the total energy use at Fort Hood, energy use in "other" types of buildings may be closer to that of commercial and institutional buildings, which is better modeled by the functional forms used by MMT models. Yet another reason could be that MMT software uses a finer search grid for the change-point than does the VBDD software. Whatever the cause, it seems that MMT is more appropriate for modeling energy and water consumption at Department of Defense (DOD) installations. Therefore, the MMT model results are used for all subsequent analyses. USE OF BASELINE MODELS FOR SCREENING In 1991, the energy office at Fort Hood instituted a successful demand-shedding initiative via frequency-modulated (FM) 2 BN (4090)

3 Figure I Comparison ofcv-rmse of different models evaluated. BN (4090) 3

4 cycling of residential air conditioning. The U.S. Army Construction Engineering Research Laboratories (USACERL) and the Fort Hood energy office wanted to baseline Fort Hood energy use sometime previous to 1991 as a means of further validating the effects of the demand-shedding effort. Hence, it was decided to use 1990 data for baseline model development at the cantonment level and for subsequent screening purposes. Once baseline models have been developed, it is possible to use them as screening tools by comparing forecast levels with actual energy use. The effect of changes in weather from year to year (more accurately, outdoor temperature) on the energy use is explicitly accounted for by the baseline model forecasts. Deviations from expectations must be studied to determine whether known extraneous changes have contributed to this variation or whether these changes are a result of energy-efficiency measures or demand-side management (DSM) programs that have been initiated. How the monthly prediction intervals (PI) of the model are to be calculated has been described in the companion paper (Reddy et al. 1997). We have used our 1990 baseline models to forecast into the future up to 1993 and also "backcast" through The year-to-year changes in building conditioned area from 1989 until 1993 were very small, about 2% to 3%, so it was not necessary to normalize energy use by this parameter in this part of the study. Figures 2 and 3 depict the extent to which the monthly utility bills are bounded by the Pis of the 1990 baseline model. For clearer visualization, we have also shown the residuals (residual = measured value minus model-predicted value) along with the Pis. If, say, the utility bill data for a month fall below the lower 90% PI, one can affirm that energy use during that month has decreased as compared to model predictions. Salient observations from each figure are reported below. Main Substation Electricity Use We note that, on the whole, the observed energy use is bounded by the Pis of the 1990 baseline model (see Figure 2). Inspection of the residual plots reveals that there are certain periods, namely, April, May, and July 1991, April through July 1992, and May through July 1993, where the observed energy use is definitely lower than the Pis of the baseline-model-predicted values (as a result of initiating the FM load management system), while energy use during September and October 1993 is higher. Main Substation Electricity Demand Figure 3 clearly indicates the benefit of the DSM program since we see a substantial reduction from March Because of the ratchet clause on the peak demand, the billed peaks in winter are also lower from onward. It is only during September and October 1993 that demand seems to have crept up again. This tracking feature of the baseline model is a useful tool for energy managers since it provides a means of ascertaining whether specific energy-efficiency measures that have been incorporated are having a noticeable impact at the whole-building or the whole-facility level and whether energy use has remained constant or has shown an inexplicable tendency to creep up from one month to the next. APPLICATION TO EIGHT ARMY BASES Data Used USACERL also sent utility data (i.e., monthly energy use data) for eight army bases from various parts of the continental U.S. (Note that the army maintains such data on a fiscal year Figure 2 Predictive ability of 1990 baseline 3P regression model for Main substation electricity use. Ninety percent prediction intervals for the model as well as for the residuals are shown. 4 BN (4090)

5 Figure 3 Predictive ability of 1990 baseline 3P regression model for Main substation electricity demand. Ninety percent prediction intervals for the model as well as for the residuals are shown. [FY] basis that extends from October of the previous year to September of the current year.) The names of the bases and their size and energy use are listed in Table 1. Data from Fort Hood, Texas, were also provided for comparative purposes in order to determine how utility and temperature data gathered from different sources would affect the baseline modeling and future tracking evaluation. Recall that Fort Hood data used in the previous sections were obtained directly from the Fort Hood energy office, which had submonitored data. There were some problems associated with monthly mean outdoor temperature data downloaded from the National Climatic Data Center at Asheville, North Carolina. One problem was that the data sets for all eight bases were from January 1985 and not from October 1984 (which is the start of FY85, the baseline year for the Presidential Executive Order). The lack of concurrent temperature and utility bill data for three months of FY85 forced us to reject FY85 as the starting year and TABLE 1 Table Giving an Indication of the Size and Energy Use of the Eight Army Bases * This value is for FY90. BN (4090) 5

6 choose FY86 instead. Further, Fort Carson, Fort Ord, and Pueblo Army Depot have temperature data until December 1993 only. Thus, the evaluation of how energy has changed over the years with respect to the baseline year has been curtailed until FY93 only (as against FY94 for the other bases). Also, temperature data for Fort Huachuca are fragmentary; therefore, the analysis was done for FY86 and FY88-FY90 only. Another significant problem was the uncertainty as to whether the monthly interval during which the daily temperature data are averaged corresponds to the utility billing period. USACERL informed us that utility reading dates are not commonly recorded in the DEIS and that, lacking further information, suggested that the start and end of the utility bill to be the first and last day, respectively, of each month. Instead, it was decided to use a procedure of inferring the utility bill read dates as described by Reddy et al. (1997) in order to systematically detect and, if necessary, correct for this potential mismatch between utility bill period and temperature data. Baseline Model Identification The procedure to identify the best regression model involves fitting each of the various MMT functional forms to the data and selecting the optimum model among the various runs based on the following criteria: The highest R 2 and lowest CV-RMSE. If R 2 values for all models are very high or very low, CV-RMSE is to be given more consideration. It would be more appropriate from physical considerations to select 3P models rather than 4P models. Only if the improvement in R 2 and CV-RMSE is substantial would a 4P model be chosen over a 3P model. Specific information on the utility bill read dates was not available from the DEIS database and so the statistical method described in the companion paper was used. We have also evaluated this methodology with billing data from several residences and office buildings where utility read dates were known, and it was found that this statistical means was able to properly identify the read dates to within three to four days in more than two-thirds of the cases. A more thorough investigation is currently under way. In the framework of this study, we have used a coarse grid consisting of the following three cases only. Case 1: We use temperature data corresponding to the same calendar month as the utility bill. Case 2: Since utility personnel may read the meter in the first few days of a month, a common oversight of a data-entry clerk who subsequently has to transfer the utility bills along with the associated month (without the exact read day) into a central database would be to associate the energy use with the end date of the utility bill period. Hence such an oversight could cause a shift of one month (even if we were to assume that utility bills corresponded to calendar month periods). Therefore, the second set of models will be run by using the temperature data of the previous month. Case 3: The above two cases still assumed that read dates more or less corresponded to calendar months. As a third search, we decided to take the average value of the temperature of the present month (concurrent with the utility bill reading in question) and that of the previous month and associate those values to the particular utility bill. If the model turns out to be substantially better than the two previous cases, this would imply that the utility bill was read sometime around the middle of the month as against the beginning. Though the same methodology can be used for increments finer than the 15-day period assumed here, we decided that such a procedure would be too laborious, and, consequently, we have limited the scope of the present search to mid- t. month corrections only. For each of our baseline models, we shall perform regressions of energy use with the three types of temperature data sets. The one that gives superior statistical fits to the utility data will be taken as the appropriate choice. In this case, both electricity and gas use during the year have been normalized with the conditioned area of the Army base during that particular year prior to model identification. The summary model statistics of our baseline model identification effort for all three cases are summarized in Table 2 for Fort Bragg and Table 3 for Fort Hood. For Fort Bragg, we notice by looking at the CV statistics that for electricity use, the 4P model with a TABLE 2 Fort Bragg: Model Identification Summary Statistics for Baseline Year (FY86) (Final Models Selected Are Shown in Boldface) 6 BN (4090)

7 TABLE 3 Fort Hood: Model Identification Summary Statistics for Baseline Year (FY86) (Final Models Selected Are Shown in Boldface) 15-day shift is most appropriate. The model chosen is excellent: R 2 = 0.95 and CV-RMSE = 4.7%. Regarding the baseline model for gas use, we note from Table 2 that using case 1, i.e., temperature data corresponding to the same calendar months as the utility bills, results in the best models. Further, there is little improvement in R 2 and CV-RMSE as we go from a 3P heating model to a 4P model, and, consequently, the 3P heating model can be chosen as our baseline model for gas use. The gas model identified has a high R 2 (= 0.87), while the CV-RMSE is also on the high side. This behavior has been observed in several other gas models as well (Reddy et al. 1996b). The R 2 statistic (which represents the fractional variation in the monthly data points about their mean annual value that is explained by the regression model) is misleading in this case due to the large seasonal variation exhibited by gas use. This factor and the fact that model Pis are directly influenced by the CV-RMSE statistic and not by the model R 2 convinced us that the CV-RMSE is a more appropriate statistic for selecting the best among competing models to be used for baselining purposes. For Fort Hood (see Table 3), we notice that the utility bill and the associated temperature data are well matched and so no correction during regression is required (i.e., corresponding to case 1 above). For electricity, the 3P cooling model seems to be the best choice for a baseline model. The model is excellent, with R 2 = 0.98 and CV-RMSE = 5.2%. Regarding the baseline model for gas use, we note from Table 3 that using case 1, i.e., temperature data corresponding to the same calendar months as the utility bills, results in the best models. Further, the small improvement in R 2 and CV-RMSE as we go from a 3P heating model to a 4P model is not enough to justify using the unphysical 4P model. Consequently, we decided to choose the 3P heating model as our baseline model for gas use. The model is also good, with R 2 = 0.98 and CV-RMSE = 10.1%. The results of our baseline model identification effort for all eight Army bases are summarized in Table 4. The model type and whether an adjustment was needed (to match the temperature data with the utility bill period) are also indicated. We note that both electricity and gas use at Fort Drum and Sacramento Army Depot needed an adjustment of one month, while electricity use at Fort Bragg and Fort Huachuca needed a 15-day adjustment. How well the models fit the data can also be ascertained from Table 4. As discussed in the companion paper (Reddy et al. 1997), the CV-RMSE of the model is the deciding factor in determining the category of the model fit. Models with CV-RMSE less than 5% can be considered excellent models, those with less than 10% can be considered good models, those less than 20% can be taken as mediocre models, and those greater than 20% are considered poor models. We note that of the eight electricity-use models, two are excellent, five are good, and only one is mediocre. Of the eight gas-use models, none is excellent, two are good, four are mediocre, and two are poor. Hence, gas-use models seem to be generally poorer than electricity-use models. Finally, we note that all gas models are 3P heating models, while electricity-use models are mixed. Thus electricity is predominantly used for heating in certain Army bases (such as Fort Drum), for cooling (for example, Fort Hood), and for both heating and cooling (at Fort Bragg and Sacramento Army Depot, where a 4P model best fits the data points). Other statistics needed to calculate prediction intervals (PI), such as the number of data points on either side of the change-point («] and n 2 ), the mean square error of the entire model (MSE), as well as mean square errors of the model on either side of the change-points (MSE] and MSE2), are also given. Inspecting the values of MSE] and MSE2 of all the models reveals that the variances of the model residuals on either side of the change-point are not constant, a fact that has been pointed out by Reddy et al. (1997) and was the basis of the need to propose statistical equations for predicting monthly and annual Pis of models that suffer from such abnormal behavior. Use of Baseline Models for Tracking Determination of percentage changes in annual energy use (normalized by conditioned area) with respect to the baseline year (FY86) permit rather well-defined conclusions to be drawn regarding the extent to which Executive Order has been met. How these changes are to be determined, as well as the 90% BN (4090) 7

8 TABLE 4 Summary of Baseline Models Identified and Relevant Statistical Measures * Units for electricity are in (kwh/d/k ft 2 ) 2 t Units for gas are in (cf/d/k ft 2 ) 2 Pis of these changes, have been described in the companion paper. Following Equation 20 of Reddy et al. (1997), we have computed the percentage changes on a year-by-year basis for Fort Bragg and Fort Hood and plotted them in Figure 4 for both electricity and gas. Note that a negative change indicates a decrease in energy use. The 90% Pis have been calculated from Equation 23 of Reddy et al. (1997), assuming no measurement error, and are also shown in Figure 4. For Fort Bragg, changes in both electricity and gas use are clearly positive. Generally, electricity use over the years, as compared to the baseline year, shows an increasing trend, with, however, FY94 use being lower than the two previous years. Gas use, on the other hand, shows a decreasing trend (a small increase in FY94 compared to FY92 and FY93), which is contrary to how electricity use behaved. However, the changes in gas use are not very significant statistically because of the wide error bands for gas. On the whole,.we note that electricity use in FY94 has increased by about 30% (±15.7%) with respect to FY86, while gas use has increased by about 10% (±28%). The uncertainty bands of the change in electricity use are relatively small, and we can be confident of our estimates of electricity change. On the other hand, there is a relatively large uncertainty in our estimates of gas use in FY94 compared to our baseline year of FY86. For Fort Hood, electricity use over the years, as compared to the baseline year, shows an increasing trend, with a rather large increase in FY94. Gas use, on the other hand, has decreased (especially from FY91 -FY94) with respect to the baseline year. Interestingly, gas use in FY94 increased compared to FY92 and FY93. On the whole, we note that electricity use in FY94 increased by about 26% (± 11 %) with respect to FY86, while gas use decreased by about 12% (±15%), i.e., the change is within the uncertainty level. Table 5 provides a summary of percentage change in energy use for all bases from FY86 until the last year of data availability (usually FY94). The table also includes the number of data points falling on either side of the change-point, the measured and model-predicted annual energy use (recall that energy use has been normalized by conditioned area and is expressed as daily annual average value), and the 90% PI intervals with and without measurement uncertainty. We note that, overall, nine of the sixteen gas and electricity use estimates are positive (i.e., energy use has increased), with five of the estimates being very large (more than 20%). Comparing the Pis with and without 8 BN (4090)

9 Figure 4 Percentage change in annual energy use per conditioned area with respect to baseline year (FY86)for Fort Bragg and Fort Hood. Negative change indicates decrease in energy use and vice versa. Ninety percent PI for the percentage change is also shown. measurement uncertainty (percentage values of 3% and 5% for electricity- and gas-measuring devices, respectively, have been used), we note that the effect of including the measurement uncertainty is very small (as pointed out in the companion paper) and could be overlooked in most cases. Finally, an inspection of Table 5 reveals that in only three of the eight bases (Carson, Huachuca, and Ord) are the annual percentage changes in electricity use smaller than the 90% prediction intervals, implying than one should not place much confidence in these estimates. Similarly, at four of the eight bases (Bragg, Hood, Huachuca, and Ord), the annual percentage changes in natural gas use are smaller than the 90% prediction intervals. Normalized Bill Comparison vs. Direct Bill Comparison We also investigated another issue with the energy-use data from Fort Bragg and Fort Hood. One could question the need to have a baselining and evaluation methodology as involved as the one adopted here, especially since the month-to-month variation patterns of outdoor temperature over the years are generally fairly consistent. One would be curious to ascertain the differences in our estimates of how energy use over the years has changed with respect to a baseline year by the present approach and by a much simpler approach involving a comparison of direct annual utility bills without any weather (see Equation 21 of the companion paper). Figure 5 illustrates the amount of differences in percentage changes between the two approaches, namely, with and without weather correction for Fort Bragg and Fort Hood. We notice that although the differences are small in certain cases (say, FY93 for electricity use at Fort Bragg and gas use during FY92 for Fort Hood), the difference is by no means negligible in cases when the percentage changes with respect to the baseline year are small. Differences between both methods are generally in the range of 3 to 6 percentage points for electricity and gas, which translates into important relative differences in the fractional changes in annual energy use. Also, there seems to be no pattern to the differences in percentage changes between both methods. The above comparison serves to under- BN (4090) 9

10 TABLE 5 Summary of Relevant Statistical Measures for Final Year of Evaluation W.R.T. Baseline Year Along with Percentage Change (a Negative Number Implies a Decrease and Vice Versa) and 90% Pis * Companion paper (Reddy et al. 1997) line the need to perform weather correction in order to obtain reliable estimates of how energy use has varied over the years. SUMMARY It was found that in most cases the MMT-based energy modeling software package performs better than the VBDD software, and even in the few cases where it did not, the difference was very small. Thus, the MMT-based package seems more appropriate for modeling energy and water consumption at the whole-facility level, which includes buildings of mixed usage. This paper illustrated the use of a statistical procedure to identify baseline models of energy use normalized by conditioned-area changes when utility bill read dates are not explicitly known. The results of applying the statistical equations for Pis both on monthly and annual time scales presented in the companion paper (Reddy et al. 1997) has been illustrated with utility bill data from eight Army bases nationwide. We have shown that our month-by-month tracking methodology is able to detect changes in energy use that are known to have occurred. The extent to which the annual energy use with respect to the baseline year has changed from the baseline year FY86 until the final year for which data were available can be determined from Table 5. We note that overall, nine of the sixteen gas and electricity use estimates are positive (i.e., energy use has increased), with five of the estimates being very large (more than 20%). Of the sixteen estimates of annual percentage change in energy use, seven are larger than the 90% prediction intervals, implying that one should not place much confidence in these estimates. The results of analyzing energy-use data of Fort Hood obtained by Submetering and from monthly utility bills from the central Army database were consistent with each other. This fact as well as the ability to identify satisfactory regression models of electricity and gas use in the various Army bases for the baseline year indicate that data obtained from "general" sources such as DEIS and NCDC databases are appropriate for developing screening tools for detecting changes in future utility bills in DOD facilities nationwide. 10 BN (4090)

11 Figure 5 Differences in percentage change in annual energy use per conditioned area with respect to baseline year (FY86) for Fort Bragg and Fort Hood determined by the present methodology and by direct utility bill comparison method. Negative change indicates decrease in energy use and vice versa. ^ ACKNOWLEDGMENTS This research was funded by the Strategic Environmental Research Development Program (SERDP), a joint effort of the Department of Energy, the Department of Defense, and the Environmental Protection Agency. We would like to thank Jamie Hebert and David Ruch of the Mathematics Department of Sam Houston State University for their insights on certain statistical issues relating to the prediction of model uncertainty bands. NOMENCLATURE CO =cooling-only model for the VBDD approach CV-RMSE=coefficient of variation of the root-meansquare error CV-STD= coefficient of variation of the standard deviation HC = heating and cooling model of the VBDD approach HO = heating-only model for the VBDD approach MMT = monthly mean temperature regression approach MSE = mean square error MSE! = mean square error of the model for data points to the left of the change point MSE 2 = mean square error of the model for data points to the right of the change point w, = number of data points to the left of the change point during model prediction m 2 - number of data points to the right of the change point during model prediction «= number of data points to the left of the change point during model identification n 2 PI = number of data points to the right of the change point during model identification = prediction intervals BN (4090) 11

12 R 2 = coefficient of determination RMSE = root-mean-square error STD = standard deviation VAR = variance VBDD = variable-base degree-day regression approach REFERENCES Fels, M.F., K. Kissock, M. Marean, and C. Reynolds PRISM (advanced version 1.0) users' guide. Princeton, NJ.: Center for Energy and Environmental Studies, Princeton University. Kissock, J.K., X. Wu, R. Sparks, D. Claridge, J. Mahoney, and J. Haberl EModel, version 1.4d. College Station: Energy Systems Laboratory, Texas Engineering Experiment Station. NCDC. n.d. Climatological data. Asheville, N.C.: National Climatic Data Center, National Oceanographic and Atmospheric Administration, U.S. Department of Commerce (electronic data or hard copy data can be obtained upon request). Reddy, T.A., N.F. Saman, D.E. Claridge, J.S. Haberl, W.D. Turner, and A. Chalifoux. 1996a. Development of baseline monthly utility models for Fort Hood, Texas. Proceedings of the Tenth Symposium on Improving Building Systems in Hot and Humid Climates, Houston, Texas: pp Reddy, T.A., N.F. Saman, D.E. Claridge, J.S. Haberl, and W.D. Turner. 1996b. Development and use of baseline monthly utility models for eight Army installations around the United States. Energy Systems Laboratory report ESL-TR-96/ College Station: Texas A&M University. Reddy, T.A., N.F. Saman, D.E. Claridge, J.S. Haberl, W.D. Turner, and A. Chalifoux Baselining methodology for facility-level monthly energy use Part 1: Theoretical aspects. ASHRAE Transactions 103(2). Saman, N.F., T.A. Reddy, J.S. Haberl, D.E. Claridge, and W.D. Turner Development of baseline monthly utility models, stabilization of data logging environment and development of metering plan and shopping list for Fort Hood, Texas. Energy Systems Laboratory Report ESL-TR-95/ College Station: Texas A&M University. USACERL Model energy installation program. Champaign, 111.: U.S. Army Construction Engineering Research Laboratories. 12 BN (4090)

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