Deriving End-Use Load Profiles Without End-Use Metering: Results of Recent Validation Studies

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1 Deriving End-Use Load Profiles Without End-Use Metering: Results of Recent Validation Studies Bedig Margossian, Quantum Consulting Inc. This paper presents the results of recent validation studies, of disaggregated end-use load profiles generated by the Heuristic End-Use Load Profiler (HELP ) developed by Quantum Consulting Inc. HELP relies on a rule-based, pattern recognition disaggregation algorithm that uses as primary input 5-minute or 15-minute premise level load data. Introduction Obtaining end-use load profiles through metering can be costly and time-consuming. As a result, the cost of an end-use metered sample designed to support a study analyzing even a relatively small number of customer segments can be prohibitive. On the other hand, most utilities maintain large samples that are metered at the premise level. Usoro and Schick 1986 present statistical methods such as conditional demand analysis that have been used to disaggregate the premise-level load data into its major end-use components. Other approaches are discussed in Battles 1990; Eto and Akbari 1990; Parti et al. 1992; and Taylor and Pratt This paper presents the results of validation studies of using a heuristic pattern recognition algorithm to disaggregate premise-level load profiles. The results in this paper are based on validation studies performed after papers presenting the results of heuristic disaggregation published by Margossian and Ellison 1993; and Powers and Martinez Heuristic Disaggregation Heuristic disaggregation has been successfully undertaken in a number of applications. The Heuristic End-Use Load Profiler (HELP ) uses as input 5-minute or 15-minute residential premise-level load research data; it also requires as input connected load estimates of the cooling, heating, and water heating appliances. The output of HELP consists of 5-minute or 15-minute residential cooling, heating, and water heating load profiles for every premise and every day in the sample used. The end-use load profiles are generated by applying a proprietary disaggregation algorithm to the premise-level load data. The disaggregation algorithm is applied to the load profile of a single customer on a single day in two stages. In the first stage, the detection stage, the algorithm scans the premise level load profile and identifies all spikes in the profile that are large enough with respect to the connected load of the space conditioning appliance. Next, the algorithm categorizes all identified spikes according to various spike attributes such as shape, timing, magnitude, and duration. In the second stage, the classification stage, the algorithm decides whether or not to attribute each of the identified spikes to the space conditioning appliance. The resulting spikes comprise the end-use load profile for the space conditioning appliance on that day. In order to derive the load profile of the water heating appliance, the algorithm first derives a residual profile for that day by subtracting the load profile of the space conditioning appliance from the premise level load profile. Next, the detection and classification stages are applied to the residual profile to derive the load profile for the water heating appliance. For example, consider Figure 1 which shows the premise level and disaggregated heuristic air conditioner load profile for a single customer on July 2, According to a customer survey, this premise has both a central air conditioner and a water heater. An analysis of the premise level load of this premise for the fifteen hottest summer days yielded a central air conditioner connected load estimate of 5.5 kw. In the detection stage, the algorithm identified the spike starting at 2:00, the spike starting at 10:00, and the spike starting at 15:00 as being significant relative to the cooling appliance connected load. The

2 Margossian smaller set of spikes starting at 4:00 were ignored. In the classification stage, only the spike that started at 15:00 was attributed to cooling. The resulting central air conditioner load profile on this day is zero up until 15:00 and 5.5 kw for the remainder of the day. The water heater load was not disaggregated in this case; however, had the water heater been disaggregated, its load profile on this day would have consisted of the 2:00 spike, the 10:00 spike, and the spike that starts at 18:00 (while the central air conditioner is operating). Figure 1 also shows the metered air conditioner load profile for this day. Validation of the Heuristic End-Use Load Profiler The Heuristic End-Use Load Profiler has been tested in several validation studies. The results of several recent studies appear below. Each of these studies consisted of two steps: first, HELP was used to derive heuristic central air conditioner load profiles from premise-level data. Second, these end use load profiles were compared to their metered counterparts collected for the same sample over the same time period. In one of the studies, the water heater load profiles were also disaggregated and subsequently compared to the corresponding metered water heater profiles. There are different criteria that one can consider in determining how accurate these heuristic end-use load profiles are; the choice should depend on how these data will be used. For example, if one is interested in using these data as input to an end-use forecasting model, then one of the criteria used should be to compare the average Unit Energy Consumption (UEC) values derived from these profiles to those obtained from the metered data. On the other hand, if the heuristic load profiles will be used to evaluate the impact of a Direct Load Control program, then it is important that the heuristic profiles be very accurate on peak and near peak days. If the profiles are to be used in estimating a central air conditioner retrofit program, then the accuracy of the average monthly weekday and weekend loads would be of interest. The results of several validation studies are presented in the figures below by comparing the heuristic load profiles to their metered counterparts. Load profile comparisons convey easily understandable information regarding the accuracy of these heuristic data since they illustrate the accuracy of this method for estimating peak load and UEC values by customer, by day, and by daytype. In each of these studies, the heuristic load profiles were derived independently of the metered load data; in other words, these were blind validation tests Figures 1 through 4 show the results of a validation study for a Southern utility. Figure 1 presents the premise-level load profile along with a comparison of the heuristic and metered central air conditioner load profiles for a single customer on July 2, Figure 2 shows a similar comparison for the same customer as in Figure 1, but for a different day, July 8, The air conditioner usage pattern is quite different on these two days. On July 2, the air conditioner comes on at around 15:00 and stays on throughout the remainder of the day. Even though this was a very hot day, the air conditioner was not used during most of the day probably because nobody was home (note that the premise level profile is low during the day). On the other hand, the air Figure 1. Comparison of Central Air Conditioner Load Profiles, Single Customer, July 2, 1991

3 Deriving End-Use Load Profiles Without End-Use Metering Figure 2. Comparlson of Central Air Conditioner Load Profiles, Single Customer, July 8, 1991 Figure 3. Comparison of Average Central Air Conditioner Loads Profiles, All Customers, July 23, 1991 conditioner is operating throughout the day on July 8; although it was not as hot as July 2 (note how the air conditioner load reflects a cycling pattern), the premise was occupied throughout the day. These two figures illustrate one of the primary advantages of this method: since the disaggregation algorithm carefully examines the premise level load profile, it takes into account information about whether the premise is occupied at the day by day, customer by customer level (see Figures 1 and 2). Figures 1 and 2 illustrate the accuracy of this method for a single customer on two separate days. Figure 3 illustrates the accuracy of the entire sample on a single near peak day, July 23, On this day, the average custom er s UEC (energy usage) estimated - by HELP and metering are 41.1 kwh and 41.4 kwh, respectively; the UEC error is under 1%. At the time of peak, the heuristic load profile is off by under 5%. A determination of whether this level of accuracy is acceptable depends, among other considerations, on the precision of the metered load profiles and on how the profiles will be used. For example, if the percentage difference between the heuristic load profile and the metered load profile is smaller than the confidence bound of the load profile derived from metering, then the heuristic profile could serve as a good substitute for the metered profile. If the profiles were to

4 Margossian Figure 4. Comparison of Average Central Air Conditioner Loads Profiles, All Customers, July 1991 be used to derive direct load control impact estimates for, say, a shedding strategy, then using the heuristic profiles would be appropriate. However, if these profiles were to be used to estimate the load impact, say, a duct repair program, then the 5% error may prove to be too great relative to the variance of the impact estimates associated with duct repair. (Please refer to Figure 3.) Figure 4 illustrates the accuracy of these profiles for the entire sample for one month, July This figure shows at the time of peak of the average heuristic profile, the heuristic profile is within 7% or the metered profile. Although the timing of the peaks is off by three hours, the profiles are never apart by more than 7%. Figures 1 through 4 show the validation results of a study involving a Southern Utility. Figure 5 shows a comparison of the average summer 1992 profiles for a different study involving a Western utility. Note that all three profiles in this figure are smoother than those shown in Figure 4; the reason for this is that the profiles in Figure 5 were based on a sample with 30 times as many premise-days as those in Figure 4. The difference between the heuristic and metered profiles at the time of metered profile peak is under 1%. This improved accuracy is due to the larger sample size; the errors resulting from spikes that are misclassified during the classification stage of the algorithm are smoothed out as the results are averaged over a large number of premises and days. (Please refer to Figure 5.) Figure 5. Comparison of Average Central Air Conditioner Loads Profiles, All Customers, Summer 1992

5 Deriving End-Use Load Profiles Without End-Use Metering In another study, a validation study was performed for a Southern utility different from the one presented above. Figure 6 shows the comparison of the metered and heuristic profiles for a sample of 50 customers for July At the time of the heuristic profile peak, the heuristic profile is within 5% of the metered profile. In a just completed study, central air conditioner load data were required to support the evaluation of Direct Load Control Programs for several utilities in the Midwest. The sample design called for end-use metering the air conditioner load for over 300 customers and using HELP to generate air conditioner load profiles for an additional 400 customers. The premise level load was also metered for a subset of the end-use metered customers; data from these premises were used for validation. Figures 7 and 8 show the validation results for the 2 utilities that had the end-use metered data used in this validation. Finally, Figure 9 shows the result of a validation study for disaggregating the water heater load for the winter season. This validation was performed as part of a study involving a third Southern utility. A large fraction of the sample consisted of homes that had electric space heating, the,,, Figure 6. Comparison of Average Central Air Conditioner Loads Profiles, All Customers, July 1991 Figure 7. Utility #1 Comparison of Average Central Air Conditioner Loads Profiles, All Customers, August 12, 1993

6 Margossian Figure 8. Utility #2 Comparison of Average Central Air Conditioner Loads Profiles, All Customers, August 27, 1993 Figure 9. Comparison of Average Water Heater Load Profiles, All Customers, Winter ed for existing load research samples; the heuristic enduse load profiles generated with this method can be used along with data from end-use metered samples to increase the overall size of the sample available for evaluating DSM programs and forecasting. remaining customers did not have electric heating appliances. This figure shows that the heuristic water heater profile matches the metered profile. This implies that the heuristic space heating profiles were also close to the metered ones (not shown in Figure 9) since for any premise with an electric space heater, the space heating load is disaggregated before the water heating load. (Please refer to Figure 9.) Conclusion This paper has shown that HELP can be used to obtain useful end-use load profiles that closely approximate those that would be obtained through metering, for utilities from different geographical locations. This method requires no new data collection since the end-use profiles are generat- References Battles, S. J Comparison Between Residential End-Use Submetering and Conditional Demand Estimates for a National Survey Performance Measurement and Analysis - Proceedings from the ACEEE 1990 Summer Study on Energy Efficiency in Buildings, Volume 10, pp American Council for an Energy Efficient Economy, Washington, D.C.

7 Deriving End-Use Load Profiles Without End-Use Metering Eto, J.H. and Akbari, H End Use Load Shape Data Application, Estimation, and Collection. Performance Measurement and Analysis - Proceedings from the ACEEE 1990 Summer Study on Energy Efficiency in Buildings, Volume 10, pp American Council for an Energy Efficient Economy, Washington, D.C. Margossian, B. and Ellison, D Obtaining End-Use Load Profiles Without End-Use Metering. Proceedings of the 6th Demand-Side Management Conference, pp. 89. Electric Power Research Institute, Palo Alto, CA. Parti, M., Sebald, A.V., Charkow, J., and Flood, J A Comparison of Conditional Demand Estimates of Residential End-Use Load Shapes with Load Shapes Derived from End-Use Meters. Proceedings from the ACEEE 1988 Summer Study on Energy Efficiency in Buildings, Volume 10, pp American Council for an Energy-Efficient Economy, Washington, D.C. Powers, J.T. and Martinez, M End-Use Profiles from Whole-House Data: A Rule-Based Approach. Proceedings from the ACEEE 1992 Summer Study on Energy Efficiency in Buildings, Volume 4, pp American Council for an Energy-Efficient Economy, Washington, D.C. Taylor, Z.T. and Pratt, R.G Purifying Mixed- Used Electrical Consumption Data. Performance Measurement and Analysis - Proceedings from the ACEEE 1990 Summer Study on Energy Efficiency in Buildings, Volume 10, pp American Council for an Energy Efficient Economy, Washington, D.C. Usoro, P. and Schick, I Residential End-Use Load Shape Estimates, Volume I: Methodology and Results of Statistical Disaggregation from Whole-House Metered Loads. EPRI Report EM-4525, Final Report.