1、AN-04-3-3 (RP-1093) Electricity Diversity Profiles for Energy Simulation of Office Buildings David E. Claridge, Ph.D., P.E. Member ASHRAE Member ASHRAE Jeff S. Haberl, Ph.D., P.E. Member ASHRAE Bass Abushakra, Ph.D. Atch Sreshthaputra ABSTRACT peak consumption that has been input. Sets of diversity
2、factors This paper presents diversity factors recommended for use in energy simulation programs that require the input of hourly usage of electricity for lighting and receptacle loads relative to the installed lighting/equipment capacity. Typical weekday and weekend profiles are presented for small,
3、 medium, and large ofice buildings. The diversityprojles were developedji-om year-long recordsji-om 46 channels of hourly or 15-minute data recorded in 32 buildings. These data were acquired and analyzed under ASHRAE Research Project RP- 1093. In addition to the typical projles developed, weekday an
4、d weekendprofiles are presented for each of the 46 channels of data analyzed. INTRODUCTION In most office buildings, internal heat gains from people, plug loads, and lighting are significant contributors to the cooling load and, hence, are very important for energy calcu- lations. Hourly energy simu
5、lation programs typically require the input of representative hourly values of the electrical consumption of lights and plug loads. The heat gains from these sources deviate from their peak values due to people entering and leaving the building who switch lights and equip- ment on and off. Some equi
6、pment, such as computers and copiers, automatically switches to “standby” or “energy saving” modes when unused for a period of time during the day. This variability in operation of ofice equipment and lighting is typically treated by inputting the peak consumption of lighting and plug loads and then
7、 using different sets of 24- hour “diversity factors” for weekdays, weekends, and any other set(s) of important daily variations. The diversity factors are numbers between zero and one that are used to multiply the given in the users manuals of individual simulation programs or estimated from engine
8、ering experience are normally used in simulation. The goal of ASHRAE Rp-1093 was to compile a library of schedules and diversity factors based on measured electric- ity consumption data for use in energy simulations and peak cooling load calculations in office buildings. This research project derive
9、d multiple sets of diversity factors from measured data in 32 office buildings. As part of the research, the methods reported in the literature for generating load profiles were carefully reviewed, and a methodology suitable for use with one year of hourly profiles was developed and implemented in a
10、 spreadsheet. The project also developed a procedure for estimating diversity profiles for loads due to occupants that is based on the diversity profiles for lights and plug loads (Abushakra and Claridge 2001). This paper presents typical weekday and weekend diver- sity profiles for weather-independ
11、ent electricity use suitable for use in energy simulations of different categories of office buildings. Typical profiles are presented for lighting, plug loads, combined lighting and plug loads, and for whole-build- ing electricity use including fans and pumps. A review of the literature on diversit
12、y profiles and methods for determining day types is given in Abushakra et al. (2004). A detailed description of the methodology employed in the project is presented in the projects final report (Abushakra et al. 2002), which also includes: the description of a spreadsheet that can be used to gen- er
13、ate similar profiles from measured hourly data for any building, a library of the schedules and diversity factors for all 32 David Claridge is a professor in the Department of Mechanical Engineering and Jeff Haberl is a professor and Atch Sreshthaputra is a Ph.D. student in the Department of Archite
14、cture, Texas A WBE-MCC: data representative of weather-indepen- dent consumption calculated by subtracting motor con- trol center consumption from whole-building electricity consumption; WBE-MCC-AHU: data representative of weather-inde- pendent consumption calculated by subtracting motor control cen
15、ter and air handler consumption from whole- building electricity consumption; WBE-MCC-Chill: data representative of weather-inde- pendent consumption calculated by subtracting motor control center and chiller consumption from whole- building electricity consumption; RECEPT submetered 120 VAC recepta
16、cle consump- tion; and LIGHT+RECEPT the sum of the previous two data streams for an individual building. LIGHT: submetered lighting consumption; It should be noted that the categories WBE-MCC, WBE- MCC-AHU, and WBE-MCC-Chill were applied only to buildings after determining that any known weather-dep
17、en- dent consumption in the building was metered in the catego- ries that were subtracted. The sixth column shows the maximum hourly load present in each one-year data set expressed in W/ fi2 and the seventh column indicates the data source. The eighth column gives the annual energy use index (EUI)
18、for the data set based on 52 weeks of weekday-weekend energy use profiles as 366 ASHRAE Transactions: Symposia Table 1. Summary Listing of the Data Sets Used to Develop the Diversity Profiles for RP-1093 TXL004 TXLOO5 TXLOO6 TXL007 TXL008 TXLOlO Category L 102,000 120,000 49 1,000 Austin, TX WBE-MCC
19、 1.83 ESL 7.59 13.36 13.38 1/1/97 Austin, TX WBE-MCC 3.13 ESL 16.46 15.73 15.80 1/1/97 Austin, TX WBE-MCC- 5.17 ESL 33.79 19.06 19.06 1/1/97 308,080 AHU Austin, TX WBE 2.76 ESL 15.95 17.40 17.35 1/1/98 15 1,620 Austin, TX WBE 1.75 ESL 12.32 20.03 20.08 1/1/98 121,654 100,000 Bryan, TX WBE-MCC 3.59 E
20、SL 19.70 15.21 15.17 7/1/98 S S L L L L M L M M M M M M S L L L L L L L L L ASHRAE Transactions: Symposia 367 Table 1. Summary Listing of the Data Sets Used to Develop the Diversity Profiles for RP-1093 (continued) presented in this paper and expressed in kW(ft2-yr). The next two columns provide “we
21、ekday” daily totals of two diver- sity factors, which are summarized in Table 2 and discussed in the next section. The final column gives the start date for the one-year data set. Each dataset was inspected for obvious outliers that were removed prior to processing. In several of the ESL sites, data
22、 removals included periods that exhibited slight weather dependency. Holidays that appeared on weekdays were also removed. Several of the LBNL sites contained synthetic or imputed data that were also removed. In each site all data removals are clearly indicated in the projects final report (Abushakr
23、a et al. 2002). All data used in this project represent measured data. Gaps were left as-is in the data sets (i.e., no data-filling nor inputing of data was performed). 368 METHODOLOGY USED TO DERIVE DIVERSITY FACTORS After performing the data quality checks, the maximum consumption value was determ
24、ined for each data channel and expressed in W/ft2. This maximum value is used to normalize all the hourly data from each site so that the data can be expressed in terms of a diversity factor with a value in the range O to 1 that is compatible with the DOE-2, BLAST, and EnergyPlus input files. In eac
25、h program, the hourly demand is calculated by multiplying the diversity factor for each hour by the maximum consumption. The data were then sorted into weekday and weekend data. Then, for each hour ofthe day, the loth, 25th, 50th, 75th, 90th percentile values, the mean, and the mean k one standard d
26、eviation were calculated and tabulated. This was separately performed for the weekday data and for the weekend data. All ASHRAE Transactions: Symposia Table 2. Summary of Comparisons Between Daily Consumption Determined from the Typical Daily Consumption Profiles and Directly from 24-Hour Sums of Di
27、versity Factors Daily Sum DFH Average Maximum Minimum 13.47 20.03 4.26 13.44 20.08 4.13 values were then normalized to values between O and 1 by dividing by the absolute maximum value in the dataset to obtain the weekday and weekend diversity factors in tabular and graphical formats. A visual inspec
28、tion of the load shapes was then used to determine if any of the profiles were incon- sistent andor contained data that needed to be eliminated (i.e., known holidays, shutdowns, etc.). For example, an unusually low minimum profile in the weekday group (which looks very much like a weekend profile) u
29、sually indicated a holiday, or a weekday profile in the weekend group usually indicated a special event. In most cases the data associated with these low or high values were removed from the dataset and the diversity factors recalculated. The dates of the removed data were recorded for each site. Th
30、e median or 50th percentile values are presented in this paper for use as the diversity factors in simulation input files. The percentile analysis was adopted instead of the more widely used mean and standard deviation approach (e.g., Noren 1997; Noren and Pyrko 1998a, 1998b) because the lighting an
31、d receptacle loads present in the office buildings studied for this project often exhibited a multi-modal distribu- tion (where the frequency curve exhibits more than one maxi- mum) rather than a normal distribution as required for mean and standard deviation analysis. Furthermore, many of the offic
32、e buildings had significant outliers that also influenced the standard deviation and the mean. Therefore the median was chosen for the diversity factors because it is less affected by outliers. The loth, 25th, 75th, and 90th percentiles are also more robust than the standard deviation when outliers
33、are present in a dataset. Percentile analysis is also more robust with datasets that tend to have a multimodal distribution (where the frequency curve exhibits few maxima). The primary purpose of a diversity profile for energy simulation is to represent the hourly distribution of the energy use. How
34、ever, the degree to which the use of the peak measured consumption for a building multiplied by the diver- sity profile represents typical daily consumption was investi- gated by determining typical daily consumption in two ways. The first was by simply computing typical daily weekday consumption; w
35、hen the typical daily weekday consumption is normalized to the maximum hourly consumption in the dataset, the resulting value is called “Daily Sum.” The second was by multiplying the hourly diversity profile values by the peak consumption; the summation of the 24 values of the hourly diversity profi
36、le is called “Daily from Hourly” or “DFH.” In both cases, these are dimensionless 24-hour totals of diversity factors. These values were calculated for the profiles determined for all 46 datasets and are summarized in Table 2. The three columns contain the average, maximum and minimum of the quantit
37、y shown in each row when all 46 datasets are considered. In general, the averages are most important for the current comparison, but the maximum, and minimum values are given as well to show the range of values encountered. The average value of the DFH sum of diversity factors for weekdays is 0.24%
38、smaller than the same sum based on daily totals. This leads us to conclude that the profiles accurately represent typical days of operation. To further test the procedure, one year of hourly predicted lighting and receptacle loads (kWWh) was calculated for building TXL004, which had no missing or ba
39、d data, using the RP- 1093 typical load shapes procedure. The predicted year was constructed by creating 52 weeks of 5 weekday load shapes and 2 weekend load shapes, filling in the known holi- days with weekend load shapes, and then multiplying the sum of these diversity factors by the peak measured
40、 consumption. The total yearly predicted consumption was 888,739 kwh while the total yearly measured consumption was 885,224 kwh-a bias error of 0.397%, which suggests that the proce- dure developed represents the measured data with accuracy at least as great as that of other simulation inputs. DIVE
41、RSITY FACTORS AND LOAD SHAPES FOR ENERGY CALCULATIONS The median or 50th percentile weekday profiles for each data set analyzed are shown in Figures 1 through 1 1. Figure 1 shows the data corresponding to the weekday lighting profiles for the three small buildings ( ci O 70 i -. 060 1 1 3 5 7 9 11 1
42、3 15 17 19 21 23 Figure 12 Lighting-only weekend profile for medium buildings. ASH RAE Transactions: Symposia 371 al w ; P + O c.i n al F 372 Bp ASHRAE Transactions: Symposia cl O o O x o O x P 8 m O o CI 2 W O 9 d O o io 2 - d o - io 3 ASHRAE Transactions: Symposia 373 diversity profiles for lighti
43、ng in medium buildings. Numerical values of the average diversity profiles for weekdays and week- ends are given in Tables 3 and 4. Numerical values of the median diversity profiles for the 46 individual data sets can be found in the RP-1093 final report (Abushakra et al. 2002). Building WAM003 week
44、end data in Figure 12 show that this building was virtually fully occupied on the weekends. Hence, while the profile is shown for this building, it is not included in the weekend average profile shown in Figure 12 or the weekend average profiles given in Table 4. Examination of Figures 1 through 3 s
45、how that while there are significant variations for individual buildings, the night- time lighting usage tends to increase as buildings get larger. Minimum lighting diversity factors for the small buildings are in the range of 0.05 to O. 1, while the minimums for the medium and large buildings range
46、 up to 0.4. Weekend lighting diversity factors show a similar pattern, increasing in the larger build- ings. Building WAM003 seems to be almost fully occupied on weekends, as noted, with its weekend peak diversity factor only a few percentage points lower than its weekday peak. Receptacle diversity
47、factors differ substantially from the lighting diversity factors as shown in Figures 4 through 6. They do show the same tendency to increase with building size, but they are much more “compressed” with the range between the minimum and maximum weekday receptacle diversity factors being about half th
48、e range for the lighting diversity factors. The combined lights and receptacle diversity factors of Figures 7 through 9 and the weather-independent WBE diver- sity factors of Figures 1 O and 1 1 are quite similar, as might be expected. The buildings shown offer considerable variation, but the averag
49、e profiles for these two groups are remarkably similar, suggesting that the averages are quite typical. CONCLUSIONS Diversity factors were developed for year-long hourly record of 46 channels in 32 office buildings. These diversity factors are composed of the hourly median or 50th percentile from the buildings in each category (i.e., small, medium, and large). The median or 50th percentile profiles shown in Figures 1 through 12 and Tables 3 and 4 are recommended as appropriate for use in energy simulations. If the users have specific knowledge of schedules for the building they wish to
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