Short-Term Forecasting in Retail Energy Markets

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3 Introduction 4 Forecasting Interval-Metered Customer Loads 5 Aggregating Interval-Metered Customer Loads 13 Forecasting Mass Market Cycle Metered Customers 16 Summary , Itron Inc. All rights reserved. 3

4 Introduction There is a clear link between accurate short-term load forecasting and the financial success of energy suppliers in retail electricity markets. The short-term retail forecasting challenge is to develop an hourly or sub-hourly load forecast for the portfolio of customers served by the energy supplier. These forecasts are used by energy suppliers to buy and sell electricity in short-term commodity markets. While the forecasting techniques used for retail forecasting are similar to those used for system operations, there are characteristics of energy retail markets that add complexity to the retail forecasting problem: Changing Customer Portfolio The portfolio of customers served by an energy supplier can change on a daily basis. In contrast, system operations forecasting is for an aggregate system load that represents a fixed short-term customer mix. Limited Real-Time Metering Portions of an energy supplier s portfolio typically contain customers with loads measured using interval meters. Unlike SCADA meters that are read continuously, the frequency of meter reads for interval-metered customers can range from real-time (i.e., every half-hour) to 30 or more days in arrears. Often, system operations forecasting models are designed to be highly autoregressive in structure to leverage the information in the real-time SCADA metering. In contrast, retail forecasting models need to be sufficiently robust to work with incomplete or nonexistent real-time metering. Limited Historical Data For the portfolio of interval-metered customers, it is likely that the historical metered data are limited in scope (e.g., one year or less of data) and in quality (e.g., missing data and spikes). The data quality places limits on the level of forecasting model sophistication that can be employed. Individual Customer Behavior Because the customer portfolio served by an energy supplier is relatively small in comparison to the aggregate portfolio served by a utility or market operator, energy supplier loads are subject to the nuances of the operating behavior of their large customers. For example, the load variation of a mining operation will have a small impact on the aggregate system load for a utility where that customer represents a small fraction of the aggregate load, but it will have a large impact on an energy supplier where that customer represents 10% or more of the customer portfolio. This introduces the need for new types of forecast drivers that predict operational behavior at the individual customer level. Profiled Customers In most retail energy markets, small customers (i.e., residential and small nonresidential customers under 10kW) have cycle-based metering, but the amount they pay for the electricity consumed is based on a market operator imposed hourly or sub-hourly consumption pattern or load profile. The assigned load profiles are used as part of the financial settlements process to spread cycle-metered consumption across the hours or sub-hours in that cycle. The challenge is forecasting the load profile to be used in the settlements process. Multiple Jurisdictions Many energy suppliers operate in more than one market jurisdiction and it is common for the market rules to vary by jurisdiction. For example, the load profiling method could be based on Net System Loads in one jurisdiction and Static Load Research Profiles in another. The energy supplier must configure their forecasting models for a diverse set of market rules. Wide Geographic Coverage For energy suppliers operating in multiple jurisdictions, it is common that their customers are spread over vast geographic regions. This places a premium on having a sufficient number of weather stations to forecast the loads of weathersensitive customers , Itron Inc. All rights reserved.

5 This paper outlines some of the approaches energy suppliers are using to meet the challenges of retail forecasting. Forecasting Interval-Metered Customer Loads As a class, large commercial and industrial customers were the first retail customers targeted by energy suppliers. In large part, this reflected the fact that, as energy markets restructured, the first customer segment given retail choice was the large commercial and industrial class. Since most of these customers were on interval meters, energy suppliers learned early on the challenges of forecasting interval-metered customer loads. The lessons learned with this class of customers are useful because many market jurisdictions require the installation of an interval meter when a customer switches from the native utility to an alternative energy supplier. Data quality and non-systematic operating behavior pose significant challenges to forecasting loads for interval-metered customers. The following are common situations that can significantly impact load forecasting results: Missing and erroneous data Load shape changes Operational changes Data start-up Missing and Erroneous Data If missing and erroneous data reads can be identified, two commonly used approaches can be utilized: weighting schemes that place zero weight on the observations in question or intelligent data filling routines that replace suspected or missing values. These approaches ensure that missing or erroneous data do not adversely impact the estimation and forecasting processes. While missing data values are identified easily, identifying erroneous data points can prove difficult. Figure 1 presents an example of missing data. Figure 2 presents an example of an erroneous data spike. Figure 1 Example of Missing Meter Data 2006, Itron Inc. All rights reserved. 5

6 Figure 2 Example of a Data Spike The standard approach for identifying erroneous data points is to pass the data stream through a series of data validation tests. Commonly employed tests include: Range checks that compare each data value to a set of upper and lower bounds. These bounds can be fixed for all intervals or allowed to vary by interval. Statistical based bounds can be constructed based on the observed load variation. Scans for zero and negative values, which are then compared to business logic that determines whether zero or negative values are legitimate. Other programmatically-based tests can be applied. These tests will catch most data errors. Experience suggests, however, that these tests should be augmented with visual inspection of the data, as part of the model development efforts. As part of the visual inspection process, the modeler should remain focused on how well a model of the data will forecast. A data spike that passes a bounds test may still adversely impact the estimated model parameters. This is the case in Figure 2 above. The data spike that occurs at the end of Sunday would pass a maximum and minimum bounds test, but it has the potential to bias the model coefficients since it will create a large model residual. From a forecasting perspective, the data spike should either be given a zero weight in the estimation process or be filled with an alternative value. In this instance, visual inspections can catch errors that algorithms cannot. Not all data spikes or outliers necessarily represent erroneous data. Figure 3 illustrates a case in which all metered data for a transportation company is shown. In addition to the July periods, there appears to be a data spike in June To determine if this is a data spike, the data is examined further by focusing on the data for June 2004, as illustrated in Figure 4. In this view, it appears that the customer s facility was operating at a higher than usual level, which leads to the apparent spike when reviewing the entire series. This is further supported by drilling into that particular week of data, shown in Figure 5. For the case of this transportation customer, the modeling challenge tries to understand why the operations were so much higher than the surrounding weeks. If that can be understood, then it is possible to construct an explanatory variable to forecast these load changes in the future. If not, then the forecast analyst must make a decision about whether this week of data should be included in the model estimation process , Itron Inc. All rights reserved.

8 Figure 5 Drilling into the Week of the Suspected Spike Load Shape Changes Another situation that arises frequently when forecasting loads for interval-metered customers is that the load pattern for an individual customer may change over time. This case is illustrated in Figure 6 through Figure 9. A quick visual comparison of the load pattern in September 2004 with that in September 2005 suggests that the customer s load shape has changed. The change in load shape patterns is more apparent when the first week in September 2004 is compared to same week in Although the load levels are similar, it is clear that the 2004 data appear manufactured when compared to the 2005 data. Fitting a model to both sets of data will result in a load shape that looks like an average of the 2004 and 2005 data. The forecast question is which data best represent the customer s current load shape. If the 2005 data are a more realistic representation of the customer s current load shape, then it might make sense not to use the 2004 data for model estimation. In this case, there is less legitimate data available for building the forecast model than appears on the surface. The following are possible causes of a change in load shape: Older data were manufactured to fill in gaps in the historical meter reads. There was a significant change in the utilization of the metered facility. For example, the occupancy rates in a large apartment building may have risen from 65% in 2004 to 85% in The customer s operations have changed from one year to the next. For example, a restaurant operating in part of the building space in 2004 might have been converted to a retail store in What can be expected is that for every interval-metered customer, there is a story that explains their load history. One of the true challenges of retail forecasting is to understand these stories in order to guide how best to forecast the customer s loads , Itron Inc. All rights reserved.

10 Figure 8 One Week in September 2004 Figure 9 Comparable Week in September 2005 Operational Changes The load patterns for many large industrial customers are tied directly to their operating schedules. Having knowledge of these operating schedules can improve these load forecasts. Figure 10 illustrates the load history for an industrial transportation equipment manufacturer. It is clear from the figure that the manufacturer reduces operations in a number of periods during the year. Figure 11 and Figure 12 illustrate a ramp-down in operations in preparation for the July 4th holiday. For major holidays, it is possible to construct a model variable to account for the load loss preceding and following these holidays. It is not always the case, however, that the load change is so predictable. In many cases, load reduction reflects the need to perform maintenance on machinery. In other cases, load swings are very short term, reflecting the turning on and off of major pieces of equipment. If maintenance and operating schedules are systematic and predictable, it is plausible to construct variables to help forecast these load changes. If they are not systematic, then these load variations are extremely difficult to predict without the customer providing additional information to the energy supplier , Itron Inc. All rights reserved.

12 Figure 12 Operations Ramp-up in the Middle of July Data Start-Up As energy suppliers recruit new customers, they typically face a start-up problem when it comes to forecasting loads for new customers. Usually there is a delay in gathering a sufficiently rich history of meter data for the customer to model their load directly. For example, if an energy supplier recruits a new high school and this customer was not previously interval-metered, then the energy supplier is responsible for installing a new meter. Once that meter is in place, it takes one year to collect a complete year of interval data that can be used to model this customer s loads. There is a parallel problem for those customers for whom a large portion of the historical load data is missing or erroneous. This latter case is illustrated in Figure 13. From a modeling perspective, the challenge posed by the data in Figure 13 is that good load information is limited for July and August. One solution to both of these cases is to forecast loads using a load shape model estimated using the load data of other customers with similar operating characteristics. For example, a load shape model constructed using the load data for three high schools with good data can be used as a proxy for the newly recruited school. This solution is described in detail in the next section. Figure 13 A Large Portion of Erroneous Data , Itron Inc. All rights reserved.

13 Aggregating Interval-Metered Customer Loads There are two compelling reasons why it makes sense to forecast interval-metered customers in aggregate. First, aggregating customers leads to a smoother aggregate load shape than if the individual shapes were aggregated together. The smoothness effect can improve forecast accuracy because the reduction in load variability allows for a richer forecast model specification. Second, aggregating similar customers is a way of developing a representative load shape for a class of customers. This representative load shape can then be used to forecast the load shape for all customers in that class. The resulting load shape must only be adjusted to account for the energy consumption of the customers in that class. This has advantages in the case where some customers have insufficient load data to model. They can be aggregated with customers of similar load shapes and forecasted as part of the aggregate. The first step in constructing a representative profile load shape is clustering customers of similar load characteristics. Examples of day-type load shapes by primary building activity are presented in Figure 14 through Figure 19. Summary load shape measures can be used to cluster customers with similar characteristics. Useful summary measures are as follows: Load Factor is defined as the ratio of average load to peak load. Large load factors are associated with relatively flat load shapes. Grocery stores and industrial facilities tend to have large load factors. Schools and office buildings tend to have low load factors. Day Fraction is defined as the ratio of on-peak energy to total energy. Schools and offices that operate primarily during on-peak hours will have large day fraction ratios. Grocery stores and industrial facilities that operate virtually around the clock have relatively small day fraction ratios. Weekend Ratio is defined as the ratio of average daily energy use on weekends to the average daily weekday energy use. Facilities with little or no operations on weekends, such as schools and offices, have low weekend ratios. Conversely, grocery stores and industrial facilities that tend to have operations on weekends have large weekend ratios. Summer Ratio is defined as the fraction of annual energy consumption that occurs during summer months. Weathersensitive facilities have relatively high summer ratios. Winter Ratio is defined as the fraction of annual energy consumption that occurs during winter months. Facilities with significant electric space heating equipment have relatively high winter ratios. Table 1 presents an example of the values these ratios are likely to have for different building types. Using the summary measures, it is relatively straightforward to assign a customer to a group with similar load characteristics. Alternatively, the customer can also be assigned to a group with similar operating characteristics if no load information is available but the type of activity that occurs at the facility is known. Building Type Load Factor Day Fraction Weekend Ratio Summer Ratio Winter Ratio Education % % 23% Office % % 22% Retail % % 22% Fast Food % % 20% Grocery % % 26% Industrial % % 25% Table 1 Load Shape Summary Measures by Building Type 2006, Itron Inc. All rights reserved. 13

14 Fall Weekday Fall Weekend Spring Weekday Spring Weekend Summer Weekday Summer Weekend Winter Weekday Winter Weekend Figure 14 Day-Type Load Shapes Education Facilities Fall Weekday Fall Weekend Spring Weekday Spring Weekend Summer Weekday Summer Weekend Winter Weekday Winter Weekend Figure 15 Day-Type Load Shapes Office Buildings Fall Weekday Fall Weekend Spring Weekday Spring Weekend Summer Weekday Summer Weekend Winter Weekday Winter Weekend Figure 16 Day-Type Load Shapes Retail Stores , Itron Inc. All rights reserved.

15 Fall Weekday Fall Weekend Spring Weekday Spring Weekend Summer Weekday Summer Weekend Winter Weekday Winter Weekend Figure 17 Day-Type Load Shapes Fast Food Restaurants Fall Weekday Fall Weekend Spring Weekday Spring Weekend Summer Weekday Summer Weekend Winter Weekday Winter Weekend Figure 18 Day-Type Load Shapes Grocery Stores Fall Weekday Fall Weekend Spring Weekday Spring Weekend Summer Weekday Summer Weekend Winter Weekday Winter Weekend Figure 19 Day-Type Load Shapes Industrial Facilities 2006, Itron Inc. All rights reserved. 15

16 Once the customer data have been aggregated into like profile groups, a forecast model of the aggregate data can be developed. Typically, the data used for the model estimation only contains customers with complete or relatively complete data histories. This results in a load shape model that can be used to forecast the profile group s load shape. If the estimation data set used a subset of the customers included in a profile group, the energy implied by the forecasted load shape will be too low. In a secondary step, the energy for the load shape can be scaled up to meet the daily energy requirements of all customers included in the profile group. Figure 20 illustrates this process. In this figure, the unadjusted load forecast is based on a model that uses the load data for five out of six restaurant customers. The sixth customer only has two months of interval-metered data. The raw unadjusted forecast is then scaled to meet the energy for all six customers. The scaled forecast is final and the results will be scheduled. Figure 20 Profile Model Scaled for the Energy of the Complete Group Forecasting Mass Market Cycle Metered Customers The vast majority of customers served by an energy supplier do not have an interval meter that measures hourly or sub-hourly consumption. The energy consumption of most residential and small commercial customers is measured on a monthly or longer time horizon. In retail markets, however, the amount these customers pay for the energy commodity is computed at the hourly or sub-hourly level. These commodity charges are calculated as part of a financial settlements process that spreads out a customer s cycle-based consumption over the hours that span the cycle read. The hourly allocation is based on a load profile that is assigned to each of the cycle-based customers. The actual load profile shape or processes for determining the load profiles vary across market jurisdictions. From a forecasting perspective, the challenge is forecasting how mass-market customers are profiled as part of the settlements process. The three parts of forecasting mass-market customers are described below. Load Shape Forecast The load shapes assigned to the customers depends on the market rules in place. There is a wide range of load shapes that are deployed, including: Net system load shape (i.e., total system loads less all interval-metered customer loads) Static profile shapes where the load shape for each day is defined one year in advance Dynamic interval metering of a sample of mass-market customers Statistical models of load research data where models are combined with weather data to generate a dynamic load profile In many instances, different profile shapes are used for different customer classes. For example, a separate load shape for residential and commercial customers is used, or separate load shapes are used for space heating and non-space heating , Itron Inc. All rights reserved.

17 customers. It is essential to understand the market rules that govern the generation of the load shapes used for mass-market customers. Average Daily Usage Estimates of average daily usage for each profiled customer are required to scale the forecasted load profile to account for the energy consumed by the portfolio of mass-market customers. There are a number of ways to develop estimates of average daily usage. The most straightforward estimates are based on actual usage history. Average daily usage values can be computed by averaging annual or monthly consumption values. Estimates that are more sophisticated use statistical models to project average daily usage values as a function of calendar and weather variables. Typically, the simple methods work well. Unaccounted-for Energy Since the profiling process is only an estimate of the true underlying energy consumption, most settlements calculations result in a certain amount of energy consumption that is unaccounted for when all the load profiles are summed up across energy suppliers. This unaccounted-for energy is usually allocated across energy suppliers in proportion to their total load. Often, these allocations account for 2 to 3 percent of the energy consumed. In other cases, the allocations can be 10 percent or higher. This represents a significant swing in the financial position of an energy provider. Obtaining an accurate forecast of the unaccountedfor energy allocations is critical. As the markets evolve, energy suppliers have a growing history of allocated load shapes for their mass-market customers. These are the load shapes derived by the settlements process. In principle, the allocated load shapes embed all three components of the load shape process. As a result, energy suppliers are using models of allocated load shapes to forecast load profiles for their massmarket customers. In so doing, it is hoped that the model can generate accurate forecasts by capturing systematic patterns in the way unaccounted-for energy is allocated. Modeling of the allocated load shapes represents the next step in forecasting loads for mass-market customers. Summary Energy suppliers have been applying an old set of methods to address the forecasting challenges in today s retail energy markets. For interval-metered customer loads, data visualization tools are invaluable for identifying data problems and suggesting possible solutions. Clustering methods can be used to aggregate customers with similar load shapes, thus reducing the number of line items that require forecasts and addressing situations where a portion of customers have limited load data. For mass-market customers, deregulated markets require mimicking the load profiling method used as part of the settlements process. In many cases, analysts can rely on standard regression techniques to forecast the profile load shapes. All are widely used methods applied to a new set of problems. 2006, Itron Inc. All rights reserved. 17

18 Itron Inc. Itron is a leading technology provider and critical source of knowledge to the global energy and water industries. Nearly 3,000 utilities worldwide rely on Itron technology to deliver the knowledge they require to optimize the delivery and use of energy and water. Itron delivers value to its clients by providing industry-leading solutions for electricity metering; meter data collection; energy information management; demand response; load forecasting, analysis and consulting services; distribution system design and optimization; web-based workforce automation; and enterprise and residential energy management. To know more, start here: Itron Inc. Corporate Headquarters 2111 North Molter Road Liberty Lake, Washington U.S.A. Tel.: Fax: Itron Inc. Energy Forecasting - West El Camino Real San Diego, California Phone: Toll Free: Fax: forecasting@itron.com Due to continuous research, product improvement and enhancements, Itron reserves the right to change product or system specifications without notice. Itron is a registered trademark of Itron Inc. All other trademarks belong to their respective owners. 2006, Itron Inc. Publication WP-02 12/06