Pattern Recognition Technologies (PRT), Inc. On-Line Load Forecasting Services

1. Pattern Recognition Technologies (PRT), Inc.On-LineLoad Forecasting Services Al Khotanzad, Ph.D., P.E. President PRT, Inc. 17950 Preston Road, Suite 916 Dallas, Texas 75252 (214) 692-5252 al@prt-inc.com www.prt-inc.com ERCOT Load Forecasting Forum January 24, 2007

2. Corporate Profile • Founded in 1994 Products & Services • Online load and price forecasting services • Stand-alone load and price forecasting software • Custom forecasting solutions Clients • Over 90 energy firms consisting of • Utilities in North America & Overseas • ISOs, Municipalities, Coops. Government Agencies • Power marketing and trading organizations • The Electric Power Research Institute (EPRI) • First company to develop a commercial neural network based load forecaster in the early 90’s – ANNSTLF for EPRI

3. Load Forecasting • Accurate forecast of future demand required by all entities involved in the energy markets • Electric Utilities • Independent System Operators • Power Marketers • Different forecast horizons • Long Term: Several years out – required for planning purposes • Mid Term: Several weeks to months – scheduling maintenance, planning fuel supply, transactions • Short Term: Next hour to next week – daily operation, energy transactions, reliability studies

4. Short Term Load Forecasting • Hourly or sub-hourly forecasts starting from next hour to next seven to ten days • Forecasts used for: • Unit commitment • selection of generators in operation • start up/shut down of generation to minimize operation cost • Hydro scheduling to optimize water release from reservoirs • Generator type coordination to determine the least cost operation mode (optimum mix) • Interchange scheduling and energy purchase • Transmission line loading • Power system security assessment • Accuracy has significant economic impact • Even a 0.5% improvement in accuracy can result in thousands of dollars in savings

5. Factors Affecting Short Term Load • Factors affecting short-term load are: • Mix of customer in the service area (residential, commercial, industrial) • Weather condition (temperature, humidity, cloud cover, wind speed) • Seasonal effects & recent load trends • Time of day (morning, afternoon, night) • Day of week (weekdays, weekends) • Holidays (Christmas, New Years) • Special events (popular sporting events or TV shows) • Demand side management • Random disturbances • Forecasts used for: • Unit commitment • selection of generators in operation • start up/shut down of generation to minimize operation cost • Hydro scheduling to optimize water release from reservoirs • Generator type coordination to determine the least cost operation mode (optimum mix) • Interchange scheduling and energy purchase • Transmission line loading • Power system security assessment • Accuracy has significant economic impact • Even a 0.5% improvement in accuracy can result in thousands of dollars in savings

6. Example of Load and Temperature

7. Major STLF Techniques • Any STLF technique attempts to model the relationship between the load and factors that affect it – these relationships are nonlinear and complex • Regression models • Stochastic time series • Spectral decomposition • Similar-day search • Intelligent system based models • Superiority of intelligent system based techniques have been demonstrated in many studies

8. PRT’s LF Technologies • Products & services are based on cutting-edge intelligent system technologies of: • Artificial Neural Networks • Fuzzy Logic • Genetic Algorithms/Evolutionary Computing

9. Artificial Neural Networks (ANNs) • Neurologically inspired systems consisting of highly interconnected elementary computational units (neurons) • Distributed processing by neurons results in intelligent outcome • ANNs learn to perform a desired task directly from examples using special training algorithms • ANNs can generalize; after training, they can produce good results for data that only broadly resembles the data they were trained on originally • ANNs are nonlinear systems, well suited for real world problems that are often nonlinear

10. Forecasting Using ANNs • A key feature of ANNs is their ability to learn a complex pattern mapping, i.e., model the underlying relationship between a set of variables and an outcome that is a function of them – • Future Load – Function of past loads and weather, recent load trends, upcoming weather, calendar effects • Train with historical data (examples of the underlying relationship) • A properly trained ANN can predict the outcome of the modeled process based on the available observations • ANN based predictors employed in a wide variety of forecasting applications such as prediction of: electric load, weather, gas consumption, stock market, economic trends time series data, future sales, traffic patterns and grade point average of students

11. Unique Aspects of PRT ANN Forecasters • Architecture of ANN specifically designed for electric load forecasting • Optimal set of inputs selected for load forecasting application • No need for frequent re-training • Quick response to deviations between forecast and actual load • Special algorithms for unusual days, e.g., weekday holidays

12. Fuzzy Logic • Fuzzy logic (FL) is a means to transform subjective/expert knowledge about a process expressed in the form of linguistic rules into computer algorithms. • FL employs fuzzy sets, fuzzy membership functions and fuzzy if-then rules to model the uncertainty in nature, and express the knowledge • A fuzzy set is a set without a crisp, clearly defined boundary, and can contain fuzzy variables with a partial degree of membership

13. Fuzzy Rule & Membership Function • An example of a typical fuzzy IF-Then rule : • IF next-day temperature is hot, and today’s temperature is hot, THEN next-day load is high • Subjective interpretation of “hot temperature” or “high load” • Characterized by fuzzy membership function – an example shown

14. Fuzzy Logic Based Load Forecaster • Develop applicable fuzzy membership functions • Extract relevant IF-THEN rules from historical data – There could be hundreds of such rules • During the forecasting phase several of the rules become activated along with some of the fuzzy membership function • Fuzzy inference engine converts all this information into a final crisp forecast

15. Genetic Algorithms (GAs) • Genetic Algorithms (GAs) are optimization algorithms that are based on the concept of natural evolution • GAs can find the optimal solution quickly and efficiently, especially when there is little information about the solution available. • GAs emulate natural evolution, and make use of four operators, including reproduction, crossover, mutation, and survival of the fittest to produce and keep the optimal solutions.

16. GA Based Forecaster • Create M sets of forecasts (in random) for a given set of actual historical data • Sort based on accuracy • Retain the top K most accurate sets (stronger solutions) and discard the rest (weaker solutions) – survival of the fittest • Use the retained K sets as parents to create a second generation of M solutions through mutation & crossover – repeat the process • After several generation, the top K solutions converge toward a single solution – Strongest solution • This is the optimal solution used as the final forecasting model

17. PRT’s e-ISOForecastPrice & Load Forecasting Service • e-ISOForecast is an on-line real-time price & load forecasting service that has been set up for all wholesale power markets/ISOs in North America • ERCOT, PJM, NY-ISO, ISO-NE, MISO, CA-ISO, ONTARIO IESO, ALBERTA AESO • Hourly forecasts for current day and six days beyond • Hourly load forecasts for one year out using various simulated weather scenarios • Forecasts are posted on www.onlineforecast.com • Subscribers use a Web browser to access and download the forecasts – available 24/7 • Forecasts are updated every hour or faster based on the most recent price/load/weather data that become available • Weather forecasts are used in the models - updated several times per day

18. e-ISOForecastPrice & Load Forecasts • ERCOT • System-Wide & Congestion Zone Load Forecasts • Zonal Market Clearing Price Forecasts • PJM • System-Wide, Regional and Zonal Load Forecasts • Real-Time & Day-Ahead LMP Price Forecasts • ISO New England (ISO-NE) • System-Wide & Zonal Load Forecasts • Zonal Real-Time & Day-Ahead LMP Price Forecasts • New York ISO (NYISO) • System-Wide & Zonal Load Forecasts • Zonal Real-Time & Day-Ahead LMP Price Forecasts

19. e-ISOForecastPrice & Load Forecasts • Midwest ISO (MISO) • System-Wide Load Forecast • Real-Time & Day-Ahead LMP Price Forecasts for Five Hubs and Various CPNs • California ISO • System-Wide Load Forecast • Zonal Supplemental Real-Time Price Forecast s • ONTARIO EISO • System-Wide Load Forecast • System-Wide Price Forecast • ALBERTA AESO • System-Wide Load Forecast • System-Wide Price Forecast

20. Forecasting Engines • Multiple models based on different technologies run in parallel generating independent forecasts • A top layer of intelligence decides to: • Select one of the forecasts as the final forecast • Combine multiple forecasts (“Combination of Experts”) into a final forecast • Accuracy is improved over use of a single modeling technique

21. Weather Data • PRT has affiliations with two major weather service providers, WSI and Meteorlogix • Most free internet based weather forecast services simply provide forecasts generated by NWS or other computer models • Weather service providers bring human meteorologists in the loop who scrutinize/edit computer generated forecasts • Weather forecasts updated several times throughout the day • Actual temperature updated every hour and with every update, new load forecasts are generated

22. Access via the Web • Forecasts are posted to a dedicated password protected page • Can be accessed using any standard Web browser from any computer • Provides easy access for all in the company • Forecasts are displayed in tabular and graphical forms • Actual data of previous day and any available data of current day are displayed • Forecasts can be downloaded in EXCEL format • Other statistics including actual prices of past week, similar day comparisons and price bands are provided

23. e-ISOForecast Main Page

24. e-ISOForecast PJM Segment

25. e-ISOForecast Load Forecast View

26. e-ISOForecast LF View - Graphical

27. e-ISOForecast LF Performance for 2006 Forecasts Recorded at 8 am CT Load: Hourly MAPE/ Daily Peak Load MAPE Temperature: Hourly MAD/Daily Peak Temp MAD

28. e-ISOForecast Performance for Forecast of Next-Day ERCOT Total Load – 2006iForecasts Recorded at 3:00 PM of Previous Day

29. Comparison of PRT and ISO LF Performance Forecasts Recorded at 8 am CT Hourly MAPE/ Daily Peak Load MAPE

30. e-LoadForecast Service • An online load forecast service for company-specific load data • Standard Service: Hourly/sub hourly forecasts for current day and six days beyond • Extended Service: Additional Hourly/sub hourly forecasts for several months and years out • User only needs to: • Provide historical load data for initial model training • Upload the most recent actual load data as it becomes available (via FTP, e-mail, provided Excel interface) • All the required actual and forecast weather data acquired by PRT from • Load and weather data quality checked and validated • Forecasts posted to a dedicated and secure website in tabular and graphical forms

31. e-LoadForecast Service, Cont’ • Forecasts are updated every hour with preceding hour’s actual observed weather • Forecasts are updated any time an actual load data is uploaded by user • 24/7 access through • Via Internet at any location • An Excel Interface with built-in functions enabling user to remotely interact with the forecasting system • FTP • E-Mail • ERCOT uses this service for forecast of its eight weather zones

32. Forecasting Engines • Multiple models based on different technologies run in parallel generating independent forecasts • A top layer of intelligence decides to: • Select one of the forecasts as the final forecast • Combine multiple forecasts (“Combination of Experts”) into a final forecast • Accuracy is improved over use of a single modeling technique

33. Weather Data • PRT has affiliations with two major weather service providers, WSI and Meteorlogix • Most free internet based weather forecast services simply provide forecasts generated by NWS or other computer models • Weather service providers bring human meteorologists in the loop who scrutinize/edit computer generated forecasts • Weather forecasts updated several times throughout the day • Actual temperature updated every hour and with every update, new load forecasts are generated

34. Other Features • The provided Excel Interface allows user to: • Modify forecasted temperatures and generate corresponding load forecasts – “What-If” scenarios • Modify predicted morning and/or afternoon peak loads. Forecasts for other hours are reshaped accordingly • View load and temperature of three most similar days (temperature wise) in the history

35. Access via the Web - View & Download • Forecasts are posted to a dedicated password protected page • Can be accessed using any standard Web browser from any computer • Provides easy access for all in the company • Forecasts are displayed in tabular and graphical forms • Actual data of previous day and any available data of current day are displayed • Forecasts can be downloaded in EXCEL format

36. Access via Excel Interface – View, Download & Interact • An Excel interface with easy-to-use built-in features • Download and view most current load and temperature forecasts in tabular and graphical forms • Modify forecasted temperatures and generate corresponding load forecasts • Modify predicted peak loads and reshape load forecasts accordingly • Download and view three most similar days • Upload actual load updates to PRT’s servers

37. Profile Based Forecasting • Retailers operating in deregulated markets work with individual accounts that may not be metered hourly (e.g., residential load) • Energy transactions and settlements are done based on hourly demand • Hourly load is simulated using pre-specified standard load profiles for client type • To forecast their retail load, load profile for each account must be scaled appropriately to account for pattern of usage by that account • Profile based module of e-LoadForecast – User provides: • List of current accounts in the portfolio along with their corresponding profile type • The historical usage data for each account • Backcasted profiles for corresponding profile types are used to develop a profile multiplier (scale factor) for each account using historical meter reads. • Forecasted standard profiles are multiplied by the scale factor to get the final hourly forecast

38. Mid-Term/Long-Term Module • Optional service includes mid-term/long-term hourly load forecast • Forecast horizon can be extended to five years out • ANN technology is used – Models are different from those used for short-term forecasting • Impact of load growth is considered • Weather forecast is needed for the forecast horizon • Simulated using historical weather data • Three scenarios of “Normal”, “Hot”, and “Cold” available for each month in forecast horizon • Additional scenarios for generating “High Load” and “Low Load” cases • Two statistical methods available for simulation of scenarios from historical weather data • Tools are provided for easy manipulation of simulated weather – user can build heat waves/cold fronts

39. Quality Control • Extensive quality control system in place • Actual load and temperature data continually quality checked • Detected anomalies such as spikes and gaps corrected • Every day accuracy of load and temperature forecasts for various forecasts horizons are computed and reviewed by our experienced staff • Corrective action taken if degradation in quality detected • Analysis of the cause • Calibrate forecasting models • Use of different kind of forecasting engines

40. Forecasting ServiceAdvantages • Uses state-of-the-art load forecasting models • More accurate forecasts than in-house systems • More economical than maintaining an in-house system • Frees up valuable manpower & resources • No data hassles, IT overhead, software maintenance & upgrade • Performance continuously monitored by specialists with extensive experience and background in forecasting • Models are continually calibrated and upgraded • Convenient access to forecasts for all who need it in the organization • Unlimited use by all in the organization