PEAK ELECTRICITY DEMAND IN COMMERCIAL BUILDINGS: A PILOT STUDY OF 1983 DATA. Eugene M. Burns Energy Information Administration1 ABSTRACT

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1 PEAK ELECTRICITY DEMAND IN COMMERCIAL BUILDINGS: A PILOT STUDY OF 1983 DATA Eugene M. Burns Energy Information Administration1 ABSTRACT This paper examines peak electricity demand in commercial buildings, using unpublished data from the 1983 Nonresidential Buiidings Energy Consumption Survey (NBECS). The NBECS obtained 1983 consumption data, in the form of monthly bilis, from the energy suppliers of a national sample of commercial buildings. For customers on demand schedules, these bills included the peak monthly demand. Using these data, the NBECS can provide descriptive estimates of the number, aggregate square footage, and annual consumption of buiidings on demand schedules relative to all commercial buildings. The NBECS can also provide more analytically interesting information, such as the peak demand levels, peak seasons, and load factors, broken down by building characteristics such as building size, principal activity, end-uses of electricity, and conservation measures. These estimates might be useful as national benchmarks for evaluating the performance of buiidings that have implemented various conservation or demand-leveling measures. The paper includes an analysis of the factors correlated with building load factors. Since this is a pilot study, the paper includes an extensive discussion of data processing and presentation issues. The methodology developed in this paper will be used to incorporate data on peak electricity demand into future NBECS Consumption and Expenditures Reports, beginning with the 1986 Report (currently in preparation). 1The opinions and conclusions expressed herein and should not be construed as representing the agency of the United States Government. are solely those of the author opinions or policy of any

2 PEAK ELECTRICITY DEMAND IN COMMERCIAL BUILDINGS: A PILOT STUDY OF 1983 DATA Eugene M. Burns Energy Information Administration 1 INTRODUCTION Data on peak electricity demand in commercial buiidings have been collected in each cycle of the Nonresidential Buiidings Energy Consumption Survey (NBECS). Neither the 1979 nor the 1983 data have been published by the Energy Information Administration (EIA), due to lack of time to study the problems involved in processing and presenting these data. However. the potential importance of the peak demand data for the analysis of energy use in commercial buiidings was recognized. An evaluation of the 1979 and 1983 NBECS peak demand data by an EIA contractor (SAIC 1985) resulted in both a revised peak demand section for the 1986 questionnaire and an in-house pilot study of data and methods (Burns 1986). This paper reports some of the methodology and findings of the pilot study. The NBECS is a two-part survey. In the -first part. data on building characteristics are obtained by interviewing the owners or managers of a nationwide sample of approximately buildings. In the second part. the NBECS acquires data on energy consumption and expenditures by requesting billing data from these buildings' energy suppliers. (Further details on survey methodology and results are contained in Energy Information Administration ( ).) For buiidings on rate schedules which take peak demand into account. the billing data include peak demand information. By combining the building characteristics data with the billing data. the NBECS can provide descriptive estimates of the number. aggregate square footage, and annual consumption of buiidings on demand schedules relative to all commercial buildings. The NBECS also can provide more analytically interesting information. such as the peak demand levels, peak seasons. and load factors. broken down by building characteristics such as building size. principal activity. end-uses of electricity. and conservation measures. As suggested by LBL (1985). these estimates could be used as benchmarks for evaluating the actual performance of buiidings that have implemented various demand-ieveling measures. The SAIC study was undertaken to evaluate the quality of the 1979 and 1983 NBECS demand data and to make recommendations for future publication and data collection efforts. Overall. the study concluded that the NBECS data can be used for demand analysis. The SAIC study did stress that NBECS demand lthe opinions and conclusions expressed herein are solely those of the author and should not be construed as representing the opinions or policy of any agency of the United States Government

3 estimates should be reported as noncoincident demand. Coincident demand is the demand simultaneously created by the set of consumers on utility resources. The bills in the NBECS data base only provide peak demand during a one-month period for each building, and these individual peaks cannot be aggregated to form an estimate of peak system demand. Furthermore, since no data are available for buiidings not on demand schedules, such an aggregation would exclude the demand of a large number of smaller buildings. This limitation means that the NBECS data are appropriate for building-level, rather than system-level, characteristics. Another important conceptual issue involves customer peak demand versus building peak demand. The NBECS is a building survey. This focus on buiidings permeates the NBECS design, including the supplier survey. Buiidings may weil have their own consumption, expenditures, and peak demands. However, only utility customers are metered, so that all consumption data about buiidings must be developed from customer records, and customers and buiidings may not coincide. In a multi-customer building, each customer with a demand meter has its own individual peak demand. In collecting data for multi-customer buildings, NBECS uses a form which requires the utility to report the total consumption and expenditures for all customers in the building. Determining peak demand for a mul~i-customer building is thus analogous to the problem of determining coincident demand for a set of buildings, except that demand data are lacking for the individual customers in the former case. Moreover, just as there are multi-customer buildings, there also are multi-building customers, where sets of bills cover more than the sampled building. The overlap between buiidings (the target population for NBECS) and electric utility customers (the population relevant to the analysis of peak electricity demand) is discussed further in Burns (1987a). While the limitations of measuring noncoincident peaks andsurveying buiidings rather than customers are inherent to any NBECS, there are problems with the 1983 (and 1979) demand data which preclude their use in anything other than a pilot study. First, the 1983 version of the supplier survey form did not ask the respondents to distinguish between billed demand and metered demand. Metered demand allows one to speak about peak demand, since metered demand is the peak actually measured. Billed demand is probably based on metered demand, but it does not necessarily represent the peak observed during the billing period. (For example, the billing demand could be the peak observed during the last 12 months, or some fraction of the metered demand, or some other formuia devised by the supplier and its local public utility commission.) Secondly, the 1983 form erroneously requested "kwh demand" instead of "kw demand." The 1986 version of the supplier survey form distinguished between billed and metered demand, and EIA is planning to publish data on peak demand in the 1986 NBECS Consumption and Expenditures Report. The data processing methodology for the 1986 data follows closely the methods for the 1983 pilot study, described in the following section, "Preliminary Data Manipulation." The format in which the 1986 data will be presented is also derived from the 1983 pilot study, and is discussed in the section on the "Presentation of Data on Peak Demand." 10.60

4 The pilot study was undertaken to develop methods for processing and presenting the NBECS demand data. Three areas were investigated: (1) data manipulation, including imputation, (2) appropriate format for the presentation of demand data in NBECS reports. and (3) substantive results from the pilot study. PRELIMINARY DATA MANIPULATION Three basic tasks must be accomplished before the data submitted by the suppliers become a file for analysis and reporting. These three steps are: (1) data editing. (2) computation Cof building peaks, load factors, etc.), and (3) imputation for missing data. This paper will provide an overview of these steps, which are fully described elsewhere (Burns 1987b). Data Editing Neither the 1979 nor the 1983 demand data had been edited prior to this pilot study. As the study progressed. it be ca me apparent that the billing dates also were in need of editing. In the 1979 and 1983 Consumption and Expenditures Reports, the billing dates had only been required to bound the report year. In general, billing periods overlapped the beginning and end of the year, and so consumption and expenditures were prorated in the beginning and ending billing periods. However, for the demand study, billing period load factors were of interest. and their calculation required that billing dates be recorded accurately. As part of the date editing, sets of bills containing unusually long periods (over 70 days) or short periods (under 14 days) were examined, and missing dates were imputed in cases where the year and month, but not the day, were reported. In addition some periods were collapsed in cases where the data indicated that the suppliers did not bill for actual consumption over a period of time. For example. there may have been one or two bills with missing consumption followed by a bill showing consumption at two or three times the normal level for that building. Af ter fixing the billing period dates. the demand data editing began by identifying the bills which were in-scope for the 1983 demand analysis. These bills either were completely contained within 1983, or more than half of the days were in Next. a load factor was computed for each billing period, as follows: consumption load factor = Idays * 24 * demand (l) The billing period load factor and peak demand were then set to missing if any of the following conditions held: 1. the peak demand was greater than or equal to the consumptioni, 2. the billing period load factor was greater than onei or 10.61

5 3. the billing period load factor was less than.01. The first condition, which is implied by the third, served to identify extreme cases of misreporting. An additional edit was performed within a building's set of in-scope bills to detect aberrant values. This edit involved testing whether the jth bill was deviant by calculating deviate = (lf(j) - LFMEAN(-j» / LFSTD(-j), lf(j) was the load factor calculated for period j, LFMEAN(-j) was the mean load factor omitting period j, and LFSTD(-j) was the load factor standard deviation omitting period j. If the absolute value of the deviate was greater than 5 and the absolute value of (lfcj)-lfmean(-j» was greater than.1, then a flag was set to indicate that the load factor is deviant. Cutoff yalues were selected af ter exam1n1ng empirical distributions of deviates and absolute differences. The load factor and demand data were not set to missing as they were for the three edits previously specified. Sets of bills containing deviant load factors were listed for analyst review. Computation Computation took place in two steps. The first classified each building into one of three demand categories (demand, nondemand, or unclassified) based on the type of demand data contained in its bilis. The second step calculated each demand building's annual peak, annual load factor, season and month of the annual peak, and the mean billing period peak and load factor. In the 1983 survey, no direct question asked either the building manager or the supplier whether a building's demand was metered. Therefore, whether a building was on a demand schedule had to be inferred from the billing data if the demand values are nonzero, then the building probably was on a demand schedule of some sort. For each demand building, the peak annual demand was calculated as the maximum demand observed among the in-scope 1983 bilis. The mean billing period peak demand and the mean billing period load factors were both calculated over the in-scope bilis, weighted by the number of days in the period. The annual load factor was then calculated as annual consumption annual load factor = , 365 * 24 * peak annual demand (2) 10.62

6 where the annual consumption was taken from the 1983 NBECS Master File. rather than recalculated from the billing data. Once the period with the peak demand was found. the peak was assigned to whichever month had the most days in that period. If a peak occurred from May through November. the building was classified as summer peaking. If a peak occurred from December through April. the building was classified as winter peaking. Some buiidings had identical peaks in both seasons. and were classified as both summer and winter peaking. Imputation The computations described above were performed using the available data. The imputation was designed to complete the data set for buiidings partially or completely lacking the required billing data. Imputation proceeded in several steps. For buiidings classified as demand buildings. one step filled in missing billing period load factors in sets of bills which were otherwise complete. These cases were relatively few in number Cless than 1 percent of all bilis) and were imputed with the mean of the billing period load factors. The imputed demand was derived from the imputed load factor by solving the billing period load factor equation (Equation 1) for the peak demand. as follows: consumption peak demand = Idays ~ 24 ~ load factor Af ter imputing a new billing period load factor and peak demand. the annual and mean billing period load factors and peaks. and the month and season of the annual peak were recomputed. If an imputed billing period peak happened to become the annual peak. then the annual peak. load factor. peak month. and peak season were flagged as based on imputed billing period data. The mean billing period load factor and peak were flagged as based on partially imputed data. An additional imputation step classified the cases left as ftunable to classifyft (including cases lacking billing data) by the computation step. logistic regression was chosen as the method for classifying the unresolved cases (Press and Wilson 1978). Examination of the data by supplier indicated astrong relationship between demand billing and annual electricity consumption. with no pattern detectable among the exceptions. Thus, the probability of being a demand building was estimated as alogistic function of annual consumption. Once the probability had been calculated, a uniform random number (in the range 0 to 1) was generated. If the random number was less than or equal to the estimated probability. then the building was imputed to be on a demand schedule. For buiidings imputed to be on demand schedules. the season of peak demand was imputed by the method of hot-decking, the same method used to impute for missing items from the building characteristics part of NBECS (EIA Appendix Cl. No attempt was made to impute the peak month for buiidings completely lacking billing period data

7 A final step imputed.annual load factors for buiidings with completely missing data. Imputed peak annual demand was then calculated by solving the load factor equation for the annual peak, as was done for billing periods. (If the mean billing period load factors were imputed, there would have been no corresponding consumption to obtain a mean peak demand for buiidings lacking billing period data.) Values were imputed using parameters estimated from a regression of the logistic transformation of the annual load factor on various building characterics (such as operating hours, end-uses of electricity, percent of floorspace heated, etc.). Separate imputation equations were estimated for each principal building activity category. PRESENTATION OF DATA ON PEAK DEMAND At the completion of the work described in the. preceeding section, the NBECS data base consisted of a national sample of commercial buiidings with the following data (reported or imputed): (1) whether demand metered, (2) peak electricity demand during 1983, (3) season of peak demand, (4) total electricity consumption during 1983, and (5) annual load factor (Equation 2), as weil as all of the data from the buiidings characteristics part of NBECS. The next issue, and, indeed, the key issue of this paper, is how best to use all of these data to serve the needs of analysts of peak demand in commercial buildings. In analyzing total annual electricity consumption, a measure commonly used is consumption per square foot. Analogously, the annual peak demand can also be analyzed as demand per square foot. The annual load factor (Equation 2), another measure of demand, relates the average consumption per hour to the peak demand. These measures can be computed for each building in the NBECS sample, but these measures need to be summarized so that groups of buiidings can be compared (e.g., by region, end-uses of electricity,principal activity). Until now, only one of the three measures discussed above has been presented in NBECS publications -- consumption per square foot calculated as a "ratio of aggregates." If a similar method were used for peak demand per square foot, the calculation becomes ~ wei HEkWCi) kw per ft 2 = = , (ratio of ~ w(i)3eft2 Ci) aggregates) (3) kwci) is the peak annual electricity demand of building i, ft2 (i) is the total square footage of building i, wci) is the sampling weight of building i, 10.64

8 and the summation is performed over i=l,...,n. Alternatively, peak demand per square foot could be calculated as a "mean of ratios," kw per ft2 = (mean of ratios) , (4) which is a weighted mean of individually calculated peak demand per square foot values. Equations 3 and 4 are both valid ways of summarizing the data, but yield different results (as discussed for consumption per square foot in EIA 1986, Appendix Cl. A variation on the mean of ratios replaces the sampling weight, wci), in Equation 4 with the product of the sampling weight and the square footage, wci)~ft2ci), so that the estimates would reflect bo th the sampling weight and the building size. However, since this estimator reduces to the ratio-of-aggregates estimator (Equation 3) in the case of peak demand per square foot Cas weil as for consumption per square foot). Interestingly, the options available for the presentation of annual load factor data are not the same as those available for peak demand per square foot. The ratio-of-aggregates estimator for the annual load factor is ~ w(i)~kwh(i) annual load factor = (ratio of aggregates) 365~24 ~ w(i)~annual peakci) CS) A conceptual problem with Equation 5 is that it apparently has the wrong denominator. If the load factor is to be interpreted as a ratio of average to peak consumption, and the denominator of Equation 5 is the aggregate annual consumption of a group of buildings, then it would seem that the numerator should be the group peak (i.e., their coincident peak), and not the sum of their individual, noncoincident peaks. NBECS cannot supply the data to estimate a group coincident peak. However, both variations of the mean-of-ratios estimator are feasible. Using the sample weight, wei), the mean-of-ratios estimator is > w(i)~( kwh(i)/(365~24*annual peak(i» ) annual load factor = (6) (mean of ratios) ~ wei) Unlike the mean-of-ratios estimator for annual peak per square foot (Equation 4), substituting w(i)~ft2ci) for wci) in the estimator for annual load factor does not reduce the estimator to a ratio of aggregates. Thus, for both the annual peak per square foot- and the annual load factor, two aggregate estimators are available: (1) a mean per building (using wei) 10.65

9 as the weighting factor) and (2) a mean per square foot (using w(i)~ft2(i) as the weighting factor). Algebraically, the latter estimator for the annual peak per square foot is equivalent to a ratio of aggregates. In the following presentation of results from the 1983 pilot study, both the mean per building and the mean per square foot are used. SOME RESULTS FROM THE 1983 PILOT STUDY Table I, using data from the 1983 NBECS, illustrates (1) the disparity between buiidings and customers, and also (2) the distribution of floorspace and consumption by demand classification. In 1983, NBECS estimated that 655 billion kwh were consumed in commercial buiidings with a total floorspace of 51,047 square feet. Almost two-thirds of the floorspace and consumption were associated with single-building customers, and about one-quarter of these buiidings were unclassified. Single-building customers could be "Unclassified" for one of two reasons: either the demand data were missing (or unuseable) on the bills or. more commonly, the electricity supplier did not provide billing data. Table I. Demand building classification (before imputation) by buildingcustomer relationship, for floorspace and electricity consumption, 1983 I Demand classification All build- IUnclass-1 Non- I ings I ified I demand I Demand (Million square feet) All buildings Building-customer relationship Single-building customer Multibuilding customer Multicustomer building Coverage unknown 51,047 23,002 7,474 20,571 32,816 8,169 6, ,839 7, ,643 6,730 6, (Billion kwh) All buiidings Building-customer relationship Single-building customer Multibuilding customer Multicustomer building Coverage unknown III S

10 Demand analysis of multibuilding customers is more problematic. These buiidings could be used for peak demand analysis if (I) the floorspace of buiidings other than the sampled buildings that are included in the bill is known and (2) the sampled building's share of the total floorspace is greater than 50 percent. Otherwise, the building is left unclassified. Similarly, multicustomer buildings and "coverage unknown" buildings lack the data needed for peak demand analysis. Table II illustrates the relationship between electricity end-uses and the season of peak demand. BuiIdings that used electricity for cooling but not heating were strongly summer-peakingj buiidings that used electricity for both heating and cooling were divided about equally between summer-and winter-peaking. Table II. Floorspace and consumption, by peak season (af ter imputation) and end-uses, E1ectricity End-Use Combinations I Season of peak All demand buiidings 1 Summer I Winter ISummer & winter IMillionlBillionlMillionlBillionlMillionlBillionlMillionlBillion 1 squarel kwh I squarel kwh I squarej kwh I squarel kwh I feet Iconsumdl feet Iconsumdl feet IconsumdJ feet Iconsumd All buiidings Cool and Heat Cool Only Heat Only Neither 33, , , , , Table III presents the consumption and demand indicators, weighted per building (Equations 4 and 6). Table III shows load factors increasing markedly with increasing annual consumption, while increasing less markedly with increasing building size and operating hours. load factors also vary by principal building activity. Finally. Table IV presents._the consumption and demand indicators by principal activity. on a mean per square foot basis (i.e. using w(i)*ft2 (i) as the weighting factor). Overall. consumption per square foot and peak demand per square foot are lower. while the load factors are higher. The measures estimated with square foot weighting are less likely to be affected by extreme buildings. 1Q67

11 Table III. Number, floorsp~ce, consumption, and consumption and demand indicators (weighted per building) af ter imputation, Building Characteristics kwh Peak IThousandlMillion IBillion Iconsumedl Watts I bui ld- I square I kwh I per I per I ings I feet Iconsumedl sq. ft. I sq. ft. Annual load factor All demand buiidings 1,847 33, Annual consumption (kwh) 10,000 or less 10,001 to 50,000 50,001 to 100, ,001 to 500, ,001 to 1 million 1 to 5 miuion Over 5 mi Uion Square footage 5,000 or less 5,001 to 10,000 10,001 to 25,000 25,001 to 50,000 50,001 to 100, ,001 to 200,000 Over 200,000 Principal activity Assembly Educational Food sales/service Health care Other Mercantile Office Residential lodging Warehouse Vacant Weekly operating hours 39 or fewer 40 to to to to 167 Always open ,366 4,134 2,671 8,564 3,974 8,531 4,009 2,092 2,514 5,128 5,075 5, III 5,171 8,164 3,420 4,716 1,365 2,126 1,716 4,723 6, ,799 4,531 1,790 2,746 5,810 7,637 6,479 4,816 5, III

12 Table IV. Consumption and demand indicators by principal building activity (weighted per square foot) af ter imputation BURNS kwh Peak Iconsumedl Watts Annua1 I per I per load I sq. ft. I sq. ft. factor All demand buiidings Principa1 activity Assembly Educational Food sales/service Hea1th care Other Mercantile Office Residential Lodging Warehouse Vacant The weighting per square foot (Tabie IV) seems preferabie to the weighting per building on two accounts: (1) to maintain consistency with estimates of consumption per square foot published elsewhere in NBECS Consumption and Expenditure Reports (as shown in the previous section) and (2) to provide estimates which are less likely to be distorted by the presence of occasional outliers. SUMMARY The NBECS peak demand data has considerable potential for the analysis of peak demand in commercial buildings. Yet, as this paper has pointed out. there also are important limitations. Conceptually. the demand peaks must be recognized as noncoincident. and the building-customer discordance must be addressed. Data processing pos~s some nontrivial editing and imputation problems. Data presentation also was discussed in detail. and illustrated with results from the 1983 NBECS pilot study. EIA plans to incorporate peak demand data into future NBECS Consumption and Expenditures Reports. beginning with the report from the 1986 survey. The concepts and methods developed during this pilot study will be the model for these future reports. Only minor. changes from the procedures presented in this paper are anticipated

13 REFERENCES Burns. E. M. (1986). "Studying Commercial Electricity Demand Using NBECS Data." Energy End Use Division. Energy Information Adminstration. NBECS Technical Note 45. (1987a). "The Analysis of Peak Electricity Demand: Population Definition and Coverage." Energy End Use Division. Energy Information Administration. NBECS Technical Note 52. (1987b). "Processing NBECS Peak Electricity Demand Data." Energy End Use Division. Energy Information Administration, NBECS Technical Note 54. Energy Information Administration (1985), Consumption Survey: Characteristics Washington. DC: DOE/EIA-0246(83). Nonresidential of Commercial Buildings Buildings. Energy (1986), Nonresidential Buildin9s Energy Consumption Survey: Commercial Buildin9s Consumption and Expenditures Washington, DC: DOE/EIA-03l8(83). lawrence Berkeley laboratory (lbl) (1985). Buiidings Energy Data Group (M. Meal and M. A. Piette), "Input to NBECS Survey Design: Power Data User Needs and Data Requirements." Memorandum to Energy Information Administration (J. Oliver). Press, S.J. and Wilson. S. (1978). "Choosing Between logistic Regression and Discriminant Analysis." Journal of the American Statistical Association. li Science Applications International Corporation (SAIC) (1985). "Nonresidential Building Energy Consumption Survey: Analysis of Demand Data." Report submitted to Energy End Use Division. Energy Information Administration, Contract DE-AC05-8l0R-20837, Task

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