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CBECS Home Page 2. Nonsampling and Sampling Errors:
Return to: 1995 Detailed Tables |
Peak electricity demand data were requested for the same billing periods for which electricity consumption and expenditures were reported. Ideally, the metered demand represented the maximum consumption rate (in kilowatts) during the billing period. However, two special data problems affected the availability of peak electricity demand data. First, although virtually all electricity consumption is metered, peak electricity demand is only metered where it is economical to do so. In general, peak demand meters are only installed for larger consumers of electricity. Second, in multicustomer buildings, each customer with a demand meter had its own peak demand. Since those peaks would rarely be coincident, the building peak cannot be taken as the sum of individual peaks. However, the overall building peak must be greater than or equal to the maximum customer peak. Following Step 2 described in the previous section “Annual Consumption and Expenditures,” the peak electricity demand data was processed in three additional steps. Step 1. Classifying building as demand-metered or not demand-metered For the 1995 CBECS, a building was considered to be demand-metered if the billing data for any account within the building showed metered peak demand. (The 1989 CBECS obtained demand-metered information from both the building respondent and the energy supplier. However, there was considerable discrepancy between the two sources of data and subsequent CBECS obtained this information only from the energy supplier.) Step 2. Determining the annual peak demand, the season of the peak, and the annual load factor for each building For single-account buildings that were determined to be demand-metered, the annual peak demand was taken as the maximum of the billing period peaks. For the few buildings that had part-year electricity billing data, the annual peak was taken as the maximum of the peaks in the reported billing periods. This approach results in a slight understatement of the annual peak, because the actual peak may have occurred during one of the unreported periods. However, since the number of buildings involved was relatively small, the difference between the part-year and full-year maxima would be small in most cases. In multicustomer buildings, the overall building peak demand was not available. However, the overall peak had to be at least as high as the highest peak reported for any single customer. In buildings where one customer’s peak was substantially larger than that of any other customer, that customer’s peak would have been close to the overall peak. Therefore, in processing bills from multicustomer buildings, the peak demand for any single customer was designated as a “partial peak” (associated with part of the building electricity consumption), although the overall building peak was still treated as missing. Before assigning the peak to a season, the month of the peak was found. Since the exact time of the billing period peak was unknown, the peak was taken to have occurred in whichever month contained the most days in the billing period during which the peak occurred. Peaks occurring in November through April were classified as winter peaks, while those occurring in May through October were classified as summer peaks. The annual load factor was then calculated, using previously calculated annual electricity consumption, as follows: annual load factor = (annual consumption)÷(365×24×peak annual demand) As an edit, the annual load factor was calculated by using the partial peak, and the partial peak was set to missing if the load factor was less than 0.1 or greater than 1. Step 3. Imputing peak demand and season of peak for demand-metered buildings Although any electricity consumer has a peak demand, three types of buildings were missing peak demand: (1) buildings not determined to be demand-metered; (2) buildings with completely missing supplier data; (3) multicustomer buildings, and other buildings with partial peaks. No attempt was made to impute for the first type of missing demand, mainly because buildings without demand-metering tended to be smaller than the demand-metered buildings, so that imputation would involve extrapolation beyond the range of the reported data. Accordingly, tables dealing with peak electricity demand have been limited to buildings with (reported or imputed) demand-metering. Once the decision was made to exclude buildings that had not been demand-metered, imputation became a two-step process. First, it was necessary to impute whether the building with missing data was demand-metered. If the building was imputed to be a demand-metered building, then the peak and season of the peak were imputed. Imputation of the demand-metering attribute made use of the relationship observed within suppliers between the presence of demand-metering and annual electricity consumption. For those buildings with reported data, the probability of being a demand-metered building was estimated as a logistic function of the annual consumption. The parameters estimated from the reported data regression were used to estimate probabilities for each unclassified building, and a uniform random number was generated. If the random number was less than or equal to the estimated probability, then the building was imputed to be demand-metered. For buildings imputed to be demand-metered, the season of peak demand was imputed by hot-decking, the same method used to impute missing items from the Building Characteristics Survey. Finally, annual load factors were imputed for each building imputed to be demand-metered. Values were imputed by using parameters estimated from a linear regression of the logistic transformation of the annual load factor on various building characteristics (such as weekly operating hours, end uses of electricity, and percent of floorspace heated). Separate imputation equations were estimated for each of nine principal building activities. The imputed annual peak demand was then calculated by solving the load factor equation for the annual peak. Load factors were imputed, and peak demand values calculated, for multiple-account buildings that had partial peaks. If the partial peak was less than the imputed peak, then the imputed peak was treated as the buildings’ annual peak demand; otherwise, the partial peak was used. Load factors and peak intensities were computed for each building reported or imputed to have metered demand. Also of interest are the analogous ratios over a utility service region, or other large area. The ratio of a region’s consumption to the annual peak for the region as a whole would represent the average utilization of the region’s generating capacity. The ratio of the region’s annual peak to the total floorspace in the region would represent the average capacity requirement per square foot. However, the regional peak cannot be determined from the individual annual (or even monthly) peaks alone, since these peaks are not coincident. That is, the individual peaks occur at different times, so that the sum of the individual peaks can be considerably greater than the overall regional peak. Top Continue to “Other Problems” Specific questions on these topics may be directed to: Joelle Michaels joelle.michaels@eia.doe.gov CBECS Manager Phone: (202) 586-8952 FAX: (202) 586-0018 URL: http://www.eia.doe.gov/cbecs/tech_errors_peak.html |